Supporting method and system for process operation

ABSTRACT

A method and a system for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.

This is a divisional of application Ser. No. 07/613,718, filed Dec. 23,1991 now U.S. Pat. No. 5,774,633, issued Jun. 30, 1998 which is acontinuation of PCT/JP90/00322 filed on Mar. 13 1990.

TECHNICAL HELD

The present invention relates generally to a method and a system forsupporting an operation of a process wherein at least one phenomenonwhich varies with time is dealt with.

BACKGROUND ART

Conventional methods of operation (or managing) various processeswherein at least one phenomenon which varies with time is dealtwith--such as water treatment processes; river information processingprocesses; meteorological information processing processes; thermal,nuclear and hydraulic power generation processes; co-generationprocesses; chemical processes; biological processes, security/exchangeinformation processing processes, and bank management informationprocessing processes--are practiced using formula models which describethese processes.

It is however impossible to convert a process into a formula modelunless casualities or causal relationships among a group of variablesdescribing the process have been made clear. On the other hand, when alogical model such as the "if then" rules is employed without using aformula model, application of such a logical model is infeasible unlessa causal relationship between causes and the corresponding results havebeen ascertained. Needless to say, even in the case of a fuzzy methodwhich makes combined use of a formula model and a logical model, itsapplication is impossible unless both the models are described. Ajudgment and/or an operation (management) has therefore been carried outin the light of precedence or past experiences in such cases. In unusualcases or the like where neither cause nor result is known, an operatorhas conducted the operation on the basis of the past phenomenologicalhistory or his memory. Accordingly, it has been difficult to conduct agood operation all the time.

Further, described generally, these methods have not yet permitted anyautomated modification of the model structure or elements (rule, etc.).It has hence been difficult to flexibly cope with an actual phenomenonwhich varies in time.

SUMMARY OF THE INVENTION

An object of the invention is therefore to provide a method and a systemfor supporting an operation of a process, which can support theoperation of the process in a steady state or a non-steady or abnormalstate by making effective use of a past history which has not heretoforebeen used effectively.

Another object of the invention is to provide a method for automaticallyextracting knowledge such as a causal relationship between a value of aninput variable and its corresponding output variable from a learnedneural circuit model.

A process operation supporting method according to the invention is amethod for supporting an operation of a process, which includesdetermination of a value of a control variable for a target, to becontrolled, in accordance with values of time-dependent input variablesso as to bring the target closer to a desired state. The methodcomprises the steps of providing a neuron circuit model of ahierarchical structure constructed of an input layer, at least onehidden layer and an output layer; causing the neuron circuit model tolearn, out of information on a past operation history of the process, atypical pattern of values of input variables at different points in timeas input signals and a value of the control variable, said control valuecorresponding to the typical pattern, as teacher signal; and inputting,as the values of the input variables, an unlearned pattern to thethus-learned neuron circuit model to determine its corresponding valueof the control variable.

The process operation supporting method according to the invention is,in another aspect, a method for supporting an operation of a process,which includes determination of a value of a control variable for atleast one target, to be controlled, in accordance with values oftime-dependent input variables such that the target can be brought intoa desired state. The method comprises the steps of providing a neuroncircuit model of a hierarchical structure constructed of an input layer,at least one hidden layer and an output layer; causing the neuroncircuit model to sequentially learn a plurality of patterns of values ofinput variables, each, by using, as input signals, patterns of values ofthe input variables at the times of attainment of control of the targetinto the desired state and, as teacher signals, values of the controlvariable, said values corresponding to the patterns; and inputting, asthe values of the input variables, a given pattern of values of theinput variables to the thus-learned neuron circuit model to determine avalue of the control variable for said given pattern.

In these process operation supporting methods, a value of the controlvariable for a present time point can also be determined by learningplural input signals through the simultaneous use of a pattern of valuesof the input variables at a given time point and a pattern of values ofthe input variables at a time point a predetermined time interval beforethe given time point as input signals and also the use of a value of thecontrol variable at the given time point as a teacher signal and thensimultaneously inputting to the neuron circuit model a pattern of valuesof the input variables at the present time point and a pattern of valuesof the input variables at a time point a predetermined time intervalbefore the present time point.

The process operation supporting method according to the invention is,in a further aspect, a method for supporting an operation of a process,which includes determination of a value of a control variable for atarget, to be controlled, in accordance with values of time-dependentinput variables so as to bring the target closer to a desired state. Themethod comprises the steps of providing a neuron circuit model of ahierarchical structure constructed of an input layer, at least onehidden layer and an output layer; causing the neuron circuit model tolearn plural input signals by simultaneously using, as the inputsignals, at least two patterns of a pattern of values of the inputvariables at a given time point, a pattern of values of the inputvariables at a time point a predetermined time interval before the giventime point and a pattern of differences between the values of the inputvariables at the former time point and those at the latter time pointand using a value of the control variable at the given time point as ateacher signal; and inputting patterns at a present time point, saidpatterns corresponding to said at least two patterns, simultaneously tothe thus-learned neuron circuit model to determine a value of thecontrol variable for the present time.

Preferably, the operation of the process is supported by extractingcausal relationships between the values of the input variables and thecorresponding value of the control valuable on the basis of the resultsof the learning by the neuron circuit model and then using the causalrelationships.

The neuron circuit model has, for example, an input layer formed ofplural neuron element models, at least one hidden layer formed of pluralneuron element models for receiving outputs from the neuron elementmodels of the input layer and an output layer formed of at least oneneuron element model for receiving outputs from the neuron elementmodels of a last hidden layer. The input variables are assigned to therespective neuron element models of the input layer and the controlvariable is assigned to the neuron element model of the output layer.The learning is performed by controlling weight factors applied toconnections between the individual neuron element models.

Further, the network of the neuron circuit model may be modified bydetermining the connection strengths between the individual inputvariables and the individual control valuables on the basis of theresults of the learning by the neuron circuit model and then using themagnitudes of the connection strengths. In this case, the connectionstrength between a specific input variable and a specific controlvariable can be defined by the sum of products of weight factors forindividual routes from the neuron element model, corresponding to thespecific input variable, of the input layer to the neuron element model,corresponding to the specific control variable, of the output layer viathe neuron element models of the hidden layer.

The modification of the network of the neuron circuit model can beachieved by eliminating the connection between particular neuron elementmodels or by using a varied number of hidden layers.

It is also possible to independently provide a neuron circuit modelwhich has learned information on a steady-state operation history andanother neuron circuit model which has learned information on anon-steady-state operation history, and in supporting the operation, toswitch over the neuron circuit models depending on whether the operationis to be in a steady state or in a non-steady state.

As an alternative, it is also possible to provide a plurality of neuroncircuit models, which have learned information on different operationhistories respectively, for variations of the pattern of values of theinput variables, and in supporting the operation, to switch over theplurality of neuron circuit models depending on the variation of thepattern of values of the input variables.

A knowledge extracting method according to the invention is a method forextracting as knowledge causal relationships between input variables andan output variable of a neural circuit model. The neural circuit modelis of a hierarchical structure constructed of an input layer, at leastone hidden layer and an output layer and having performed learning alimited number of times by determining weight factors betweenmutually-connected neuron element models in different layers of theinput layer, hidden layer and output layer. With respect to pluralroutes extending from a neuron element model, corresponding to aparticular input variable, of the input layer to a neuron element model,corresponding to a particular output variable, of the output layer byway of the individual element models of the hidden layer, a product ofthe weight factors for each of the routes is determined, and theproducts for the plural routes are summed, whereby the sum is employedas a measure for the determination of the causal relationship betweenthe particular input variable and the particular output variable.

A process operation supporting system according to the invention is asystem for supporting an operation of a process, which includesdetermination of a value of a control variable for a target, to becontrolled, in accordance with values of time-dependent input variablesso as to bring the target closer to a desired state. The systemcomprises a processing means having a neural circuit model of ahierarchical structure constructed of an input layer, at least onehidden layer and an output layer, said neural circuit model having beenallowed to learn results of an actual operation in the past by usinginformation on the history of the past operation as input signals and ateacher signal; an input means for obtaining, from the target, inputvalues of the input variables to be inputted to the neural circuitmodel; a knowledge extraction means for extracting knowledge from thelearned neural circuit model; a knowledge base for accumulating theknowledge obtained by the knowledge extraction means; an inferencesystem for obtaining process-operation-supporting information from theknowledge accumulated in the knowledge base; and a guidance means forperforming guidance of the control of the target in accordance with anoutput from the processing means and/or an output from the inferencesystem.

Incidentally, the term "process" as used herein embraces variousprocesses wherein at least one phenomenon which varies with time isdealt with. The term "operation" should be interpreted in a broad senseso that it may mean operation, management, control or the like. Further,the term "to support an operation" means, in a narrow sense, to supportan operator upon operation of a process but in a broad sense, includesdirect control of a process without relying upon an operator.

The invention applies a learning function of a neural circuit model to(a) learning from information on an operation history of a process, (b)acquisition of history information as knowledge and (c) automatedconstruction of a model describing the history information.

In causing a neural circuit model to learn, the neural circuit model isallowed to learn only typical patterns in each of which a successfuloperation was performed. This makes it possible to provide the neuralcircuit model with decision-making ability comparable with operatorshaving abundant experiences, whereby the neural circuit model canperform a suitable support, which conforms to the past results andprecedence, in response to an actual given pattern of input values ofvariables. Moreover, the ability of the neural circuit model can beprogressively improved in the light of reality by allowing the neuralcircuit model to continue learning after the initiation of an actualoperation of the process. It is therefore possible to ultimately aim ata uniform and optimal operation without relying upon the experiences,ability, quality and the like of each operator.

In some processes, the optimal value of a control variable may vary evenfor the same pattern of the values of the input variables depending uponwhether the values of individual input variables are increasing ordecreasing. With the foregoing circumstances in view, the simultaneoususe of not only information on the operation history at a given timepoint but also information on the past operation history at a time pointa predetermin˜ed time interval before the given time point orinformation on the differences therebetween makes it possible tosuitably support an operation in compliance with variations of aprocess. Still more effective support of an operation is feasible byproviding discrete neural circuit models which have learned informationon an operation history in a steady state, information on an operationhistory in an abnormal state, and the like, respectively.

In addition, there is possibility that knowledge, such as causalrelationships which an operator is not aware of, may be contained ininformation on a past operation history. However, causal relationshipsbetween many input variables and control variables are not clear. Payingattention to the magnitudes of weight factors and the connections in aneural circuit model which has already learned, the present inventionhas hence made it possible to automatically extract and obtain therelationships between causes and the corresponding results. As a result,it is possible to automatically or semi-automatically put pieces ofknowledge, which are contained in information on operation historieswithout being noticed, into a data base or a knowledge basesuccessively. Here, the term "semi-automatically" means an interactivemanner of operation by the operator. Operations of various processes ineach of which at least one time-dependent phenomenon is dealt with canalso be supported by such knowledge bases.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one embodiment of the present invention asapplied for supporting an operation of a water purifying plant;

FIG. 2 is a flow diagram of specific procedures in the embodiment;

FIG. 3 is a schematic diagram of a neural network;

FIG. 4 is a schematic diagram of a neuron element model;

FIG. 5 is a diagrammatical illustration of conversion of a signal by theneuron element model;

FIG. 6 is a schematic diagram of a neural network useful for`perception`;

FIG. 7 is a flow diagram of another embodiment of the present invention;

FIG. 8 through FIG. 10 are schematic diagrams of neural networksconstructed to apply the present invention to the injection of aflocculant;

FIG. 11 through FIG. 13 are block diagrams of a further embodiment ofthe present invention;

FIG. 14 is a block diagram of a still further embodiment of the presentinvention;

FIG. 15 is a block diagram of a still further embodiment of the presentinvention;

FIG. 16 and FIG. 17 are schematic illustrations of a still furtherembodiment of the present invention; and

FIG. 18 and FIG. 19 are schematic illustrations of a still furtherembodiment of the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

The present invention comprises the steps of: (1) causing a neuralcircuit model (neural processor) to learn typical patterns at differentpoints in time out of patterns of values of multiple time-dependentinput valuables, (2) supporting an operation on the basis of the resultsof the learning, (3) obtaining knowledge from the results of thelearning and accumulating candidate knowledge, (4) diagnosing thereasonability of each candidate knowledge as knowledge, and (5)supporting the operation on the basis of the knowledge and inference.The patterns are patterns of multiple variables in past operation(s) orpresent operation. Although their detail description will be made below,the term "multiple variables" may include every variable which varieswith time. The present invention can therefore be applied to variousinformation processing processes or plants. Here, one embodiment of thepresent invention as used in the support of control of a water purifyingplant will hereinafter be described with reference to FIG. 1.

First of all, the construction and operation of the entirety of FIG. 1will be described.

A description will firstly be made of the flow of the water purifyingplant. In FIG. 1, raw water derived from a river, lake or pond (notshown) is introduced into a receiving basin 9. The raw water is thenguided from the receiving basin 9 to a high-speed mixing basin 10, wherea flocculant (polychlorinated aluminum, aluminum sulfate or the like) ina liquid form is injected by a flocculant feed pump 12A from aflocculant tank 11A. To promote formation of flocs, an alkali agent suchas calcium hydroxide or sodium carbonate is also injected by an alkaliagent feed pump 12B from an alkali agent tank 11B. Inside the high-speedmixing basin 10, a stirring blade 14 is driven by a stirrer 13 so thatfine particles of about 0.01 mm floating in the raw water are convertedinto microflocs of about 0.1 mm. The water is then introduced into afloc-forming basin 15, in which the microflocs are allowed to grow intoflocs. The floc-forming basin 15 is composed of plural basins 15A, 15Band 15C. The individual basins are provided with stirring paddles 17A,17B and 17C which rotate slowly. By this stirring, the microflocs areallowed to grow into flocs of about 1 mm in diameter. The flocs thusgrown are allowed to settle in a settling basin 16, and the supernatantis filtered in the filtering basin 17. From a washing water tank 18,back washing water is intermittently jetted into the filtering basin 17by a pump 19, whereby a filter bed is washed. Filtered water istemporarily stored in a purified water basin 20 and a water distributionbasin 21, and is then delivered by a pump 22 to individual customers byway of a distributing pipe network 24. Valves 23A, 23B and 23C areprovided to control the pressure and flow rate. For sterilization,chlorine is also injected by a chlorine feeder 26 at suitable rates intothe receiving basin 9 and purified water basin 20 from a chlorine tank25.

A description will next be made of instruments. To measure the qualityof raw water, the receiving basin 9 is equipped with a measuringinstrument 5A. Measured by the instrument 5A are water temperature,turbidity, alkalinity, pH, electrical conductivity, residual chlorineconcentration, chlorine demand, water quantity, water level, and thelike. The floc-forming basin 15C is provided with an image pickup means5B such as a marine camera. Image pickup means may also be provided, asneeded, in the high-speed mixing basin 10, the floc-forming basins 15A,15B and/or the settling basin 16. The settling basin 16 is provided witha turbidimeter 5C. Measured by a measuring instrument 5D for thefiltering basin 17 include head loss, water level, turbidity, residualchlorine concentration, pH, turbidity (sic.), flow rate, and the like.The purified water basin 20 and water distribution basin 21 are providedwith instruments 5E and 5F, respectively, which measure water level,turbidity, residual chlorine concentration, pH, turbidity (sic.), flowrate, water pressure, water temperature, etc. The water distributingpipe network 24 is equipped with a measuring instrument 5G, whichmeasures turbidity, residual chlorine concentration, pH, flow rate,water pressure, water temperature, and so on.

Input of these measurement values to a computer system 80 and theprocessing and storage of the former by and in the latter will next bedescribed.

Data of the various measuring instruments described above are inputtedto the computer system 80. Various data obtained by the measuringinstruments 5A, 5C, 5D, 5E, 5F and 5G are sampled out at predeterminedtime intervals (1 minute or 1 hour). Their respective signals 5AS, 5CS,5DS, 5ES, 5FS and 5GS are inputted at an input port 56A, and are storedin a memory 54 via a system bus 52. The memory 54 also stores variousprocessed data which have been processed by a system processor (SP) 42.Incidentally, electrical analog signals 5BS which have been obtainedfrom the image pickup means 5B and represent a halftone image of flocsare converted into digital signals in an image processor 40, and arethen subjected to image processing. A monitor 50 outputs the results ofprocessing by the image processor 40.

Here, the image processor 40 has the function to perform variousprocessings which the present inventors have proposed to date (forexample, Japanese Patent Application No. 82952/1986), and its outlinewill be described hereinafter. A halftone image obtained by the imagepickup means 5B is binarized and converted into a binary image. Fromthis binary image, the area, volume, representative diameter,configuration and the like of each floc are calculated. Calculated nextare characteristic quantities such as the particle size distribution,the number of flocs, statistic representative particle sizes in thedistribution, the width of the distribution (standard deviation, etc.),configurational characteristics of the flocs, the quantity of the flocsformed, the brightness of the flocs, the brightness of the background,and the density of the flocs. The overall processing may be monitoredusing values of the measuring instruments 5A, 5C, as needed.

Next, the construction of the computer system 80 will be described infurther detail. Connected to the system bus 52 are the system processor42, the image processor 40, the memory 54, a neural processor (NP) 70, aknowledge base 60A, a candidate knowledge base 60B, and an inferencesystem 61. To the system processor (SP) 42, a keyboard 44 and a display46 are connected. Input items from the keyboard 44 include (1) operationconditions for the image processor 40, (2) calculation conditions forthe neural processor 70, and (3) operators knowledge on water qualityinformation and image information. An additional keyboard may also beprovided exclusively for the setting of the operation conditions (1) forthe image processor 40. The system processor 42 systematically controlsthese conditions (1), (2) and (3), and controls the operation of theinference system 61 as needed.

A description will next be made of outputs from the computer system 80.The results of processing by the computer system 80 are transmitted viaan output port 56B to the flocculant feeder pump 12A, alkali agentfeeder pump 12B, stirrer 13, stirring paddles 17A, 17B, 17C, pump 19,chlorine feeder 26, pump 22, valves 23A, 23B, 23C, etc. as outputcontrol signals 12AS, 12BS, 13S, 17AS, 17BS, 17CS, 19S, 26S, 22S, 23AS,23BS and 23CS. These signals control the operations of the correspondingdevices. A control item to be performed by each device will hereinafterbe described.

The injection of a flocculant is controlled by the flocculant feederpump 12A, the injection of an alkali agent by the alkali agent feederpump 12B, high-speed stirring by the stirrer 13, low-speed stirring bythe stirring paddles 17A, 17B, 17C, back washing by the pump 19,pre-chlorination and post-chlorination by the chlorine feeder 26,distribution flow rate and pressure by the pump 22 and the valves 23A,23B, 23C. Besides, various controls relating to the maintenance andmanagement of the water purifying plant such as flow rate and waterlevel control are also performed although their details are omittedherein.

The outline of a control method will hereinafter be described. Thecontrol method can be divided into (I) a conventional control method inwhich relationships between pieces of measurement information or betweenmeasurement information and output information (control information) areconverted into a model on the basis of the measurement information andoutputs are controlled based on the model and (II) a supporting methodwhich relies upon learning of a history. The term "controls" as usedherein mean automatic operations to be performed without enquiry to theoperator. Further, the term "support" means an operator guidance, andprimarily indicates an operation which performs a control only when anoperator's approval is obtained after reference data and an operationguidance are reported using the display 46 (or a voice generatordepending on the processing).

A description will next be made of the outline of procedures when themethod (II) is performed. In the present embodiment, the support by themethod (II) comprises the following five steps: (1) learning the historyof various measurement information by the neural processor 70, (2)supporting an operation based on the results of the learning, (3)extracting knowledge and candidate knowledge from the results of thelearning, (4) diagnosing the reasonability of the candidate knowledge,and (5) supporting the operation based on the knowledge and inference.Here, the term "knowledge" indicates a certain rule for the correlationbetween a cause and a result. Knowledge includes empirical knowledge andscientific knowledge. On the other hand, the term "candidate knowledge"indicates the combination between an event A and another event B (theevent B occured when the event A was satisfied). Such a particular pairof events may occur by chance, so that it may not be considered asinevitable or empirical knowledge in many instances. It cannot thereforebe recognized as knowledge. However, the pair of the events can berecognized as knowledge when they occur in combination many times. Theseknowledge and candidate knowledge are stored in the knowledge base 60Aand the candidate knowledge base 60B, respectively.

In some instances, it is possible to use only the learning step and thesupporting step.

Specific procedures of each of the control methods will next bedescribed. A description will first be made of the control method (I),which relies upon a model phenomenologically representing therelationships between variables. This is a conventional method and, forexample, using as input variables the quality of raw water measured bythe instrument 5A (water temperature, turbidity, alkalinity, pH,electrical conductivity, residual chlorine concentration, chlorinedemand, water quantity and water level), determines as a function ofthese variables the amount of a flocculant to be injected. As a modelfor the above determination, there is used a formula which has beenascertained through experiments or experiences. For example, regardingthe turbidity, the amount of a flocculant to be injected is increased asthe turbidity becomes higher. This control operation is performedwithout enquiry to the operator. A detailed description of this methodis omitted herein because this is a conventional technology see, forexample, "SUIDOKYOKAI ZASSHI (Journal of Water Service Workers'Association)", No. 431, Page 28 (August, 1970)!.

A description will next be made of details of the supporting method(II), which relies upon the learning of an operation history, and itsprocedures. As its prerequisite, the concept of a "support" in thepresent invention will be described.

The supporting method of the present invention is to give a guidance toan operator by a diagrammatic display or to automatically perform anoperation in order to obtain substantially the same results as thoseavailable when the operator recalls the information on a past operationhistory and conducts the operation on the basis of his memory. Forexample, an operator is well aware of typical patterns of values ofplural variables X_(i) through experiences. Here, the term "pattern"means a group of values Y_(i) of variables X_(i) at a given time point.In addition, when an unusual, i.e., abnormal phenomenon occurred inconnection with a certain variable X_(i), the operator is also wellaware of the phenomenon. If an unusual (abnormal) phenomenon alsooccurred at the same time with respect to another variable X_(j), heestimates causes for these phenomena in various ways. Assume that thevariable X_(i) is a variable to be controlled and the other variableX_(j) is a variable for controlling the variable X_(i). If the operatorhas the experience that the variable X_(i) was successfully controlledby an operation of the variable X_(j) in a past abnormal time, he wouldprobably perform the operation in view of the past experience or in asimilar manner. Although he can perform the control in exactly the samemanner provided that the variable X_(i) appears in exactly the samepattern, the pattern of the variable X_(i) fractionally differs as amatter of fact. It is therefore feasible for a man to perform theoperation to give good results, but it has been difficult to realize itautomatically. A man remembers a past history as experiences andtherefore judges situations systematically on the basis of theexperiences. The present invention is to provide a method forautomatically performing such an operation. In this embodiment, theoverall operation of the water purifying plant will be described.

The support according to the method (II) comprises the steps (1)-(5) asdescribed above. In the learning step (1) designated at numeral 71, pastpatterns P₁(t₁), P₂ (t₂), . . . P_(i) (t_(i)) will be described below!of various measurement information are learned from a pattern file 71Sby the neural processor 70. A method for the selection of past patternsto be learned will be described subsequently. In the supporting step (2)designated at numeral 72, the operation is supported based on theresults of the learning. In the knowledge extraction step (3) shown atnumeral 73, knowledge and candidate knowledge are extracted from theresults of the learning. In the knowledge diagnosing step (4)illustrated at numeral 74, a diagnosis is effected to determine whetherthe candidate knowledge is reasonable as knowledge. In the operationsupporting step (5) indicated at numeral 75, the operation is supportedbased on a group of knowledge obtained by the leaning and a group ofknowledge inputted in advance. These processes (2)-(5) are performed bythe system processor 42, and if necessary, the keyboard 44 and display41 can be operated and the knowledge base 60A and the candidateknowledge base 60B can be accessed.

Incidentally, the term "knowledge" as used herein is a rule of the type"if . . . , then . . . ".

Contents of each of the steps will hereinafter be described in detail.

The learning step (1) 71 will now be described. Since variables dealtwith as information on an operation history include all variables storedas data, they will be described first of all. The term "variables" asused herein include those inputted through the input port 56A, such asthe water temperature, turbidity, alkalinity, pH, electricalconductivity, residual chlorine concentration, chlorine demand, waterquantity and water level measured by the measuring instrument 5A; thefloc particle size distribution, the number of locks, the statisticrepresentative particle sizes of the distribution, the distributionwidth, the configurational characteristics of the flocs, the amount ofthe flocs formed, the brightness of the flocs and the brightness of thebackground calculated by the image pickup means 5B and the imageprocessor 40; the turbidity measured by the turbidimeter 5C; the headloss, water level, turbidity, residual chlorine concentration, pH andflow rate measured by the measuring instrument 5D; the water levels,turbidities, residual chlorine concentrations, pH's, flow rates, waterpressures and water temperatures measured by the measuring instruments5E and 5F of the purified water basin 20 and the water distributionbasin 21, respectively; and the turbidity, residual chlorineconcentration, pH, flow rate, water pressure and water temperaturemeasured by the measuring instrument 5G of the distributing pipe network24. These variables will be represented by X_(i). Further, a group ofvalues Y_(i) which these variables X_(i) take at a given time point t₁will be represented, as pattern 1, by P₁ (Y₁ (t₁), Y₂(t₂), . . . Y_(n)(t_(n))). This may be abbreviated as P₁ (t₁). Patterns P₁(t₁), P₂ (t₂),. . . at different time points are learned. To make a generalizeddescription, these variables are all inputted and learned in thisembodiment. Needless to say, these variables can be selectively used inaccordance with the objective.

Values of these variables are processed (learned) at the neuralprocessor 70. This processing will be described with reference to FIG.3.

Marks will be described firstly. In FIG. 3, each mark "O" indicates aneuron element model 701. A solid line 702 connecting one "O" to another"O" indicates the existence of transmission of information between theassociated neuron element models 701. Further, the neural processor 70comprises an input layer 710, a hidden layer 720 and an output layer730. Here, each layer is constructed of a limited number of neuronelement models, while the neuron element models in the respectiveadjacent layers are connected one another. Although the hidden layer 720can include plural layers, an example having only one hidden layer isshown by way of example in the present embodiment for the sake ofsimplification. FIG. 3 illustrates the construction at the time oflearning while FIG. 6 shows the construction at the time of use afterthe learning. These constructions will be called neural networks (neuralcircuit models or neural circuit networks).

A description is now made of the setting of the variables for the inputlayer 710 and the output layer 730. Variables to represent causes areassigned to the input layer 710, while control variables to bedetermined based on these causes (i.e., variables to be controlled) areassigned to the output layers 730. In other words, as variables for theoutput layer 730, control variables are set in the present embodiment.

The neural network will be described more specifically. Firstly, theabove-described pattern P_(i) (t_(i)) is inputted from the pattern file71S to the individual neuron element models of the input layer 710. Itis desirable to adjust the scale of the pattern so that the values ofthe pattern are "0" or greater at the minimum but are not greater than"1" at the maximum. On the other hand, signals outputted from the outputport 56B are assigned to the individual neuron element models of theoutput layer 730 and also to a teacher signal layer 750. Namely, thesesignals include the signal 12AS for the control of the flocculant feederpump 12A, the signals 12BS for the control of the alkali agent feederpump 12B, the signals 13S for the control of the stirrer 13, the signals17AS, 17BS, 17CS for the control of the stirring paddles 17A, 17B, 17C,the signal 19S for the control of the pump 19, the signal 26S for thecontrol of the chlorine feeder 26, the signal 22S for the control of thepump 22, and the signals 23AS, 23BS, 23CS for the control of the valves23A, 23B, 23C. A target pattern composed of the values of these signalsis represented by C(P_(i) (t_(i))).

Here, the basic operation of the neuron element model 701 will bedescribed with reference to FIG. 4, in which inputting of n variables(X₁ -X_(n)) is illustrated by way of example. Values of the signals forthe variables X₁ -X_(n) at a given time point t₁ are expressed by Y₁(t₁)-Y_(n) (t_(n)).

A setting method at the time point t₁ will be described firstly. Assumethat the pattern at the time point t₁ is P₁ (t₁). For some patterns,this time point t₁ is selected by the operator. For other patterns, itis selected automatically. The former patterns include the typicalpattern P₁ (t₁), and patterns at abnormal times which the operator wantto refer to subsequently. They are stored in the pattern file P_(i)(t_(i)). This selection is important because the neural network willeventually function on the basis of these learned contents. Theselection is assigned to the operator with a view to relying upon thesystematic data judgment ability which the operator has obtained throughexperiences. In this case, the patterns to be learned are pluralpatterns P₁ (t₁), P₂ (t₂), . . . at different time points, and theneural network is cause to learn these plural patterns repeatedly. As aresult, it is possible to have the neural network equipped with apattern recognition ability comparable with that of an operator havingthe past experiences. Teaching by the operator is conducted byman-machine conversation via the keyboard 44 and the display 46.

On the other hand, automatic selection requires an advance statisticalanalysis of a data stream. Namely, a pattern which occurs mostfrequently is determined by a statistical analysis. Regarding thispattern as a pattern for a steady state, the neural network is caused tolearn it. Another pattern which occurs least frequently is thenconsidered as a pattern for an abnormal state, and the neural network isalso caused to learn the pattern.

As a specific method for the selection of patterns, the following methodis also effective.

(1) When the treatment has been succeeded (for example, the turbidity ofthe settling basin is 1 mg/M or lower): The neural circuit model iscaused to learn the conditions for this operation either automaticallyor after enquiry to the operator. (2) When the treatment has resulted ina failure: The neural circuit model is not caused to learn the conditionfor this operation.

By repeating these operations (1) and (2), the neural circuit model canselectively learn the conditions for successful operations only. Thishas the advantage that the neural circuit model becomes wiser as thelearned contents increase.

Namely, there is the advantage that the perception error (to bedescribed below) will be reduced gradually and the accuracy will beimproved correspondingly.

A method for causing the neural circuit model to learn given patternswill be described over a some length of pages hereinafter.

Further, although will be described in detail, the objects of thepresent invention can be attained more effectively, for example, bysimultaneously inputting patterns P₁ (t₁), P₂ (t₂), . . . of variablesat such different time points to the input layer, also inputting thetime-dependent differences P_(i) (t_(i))-P_(j) (t_(j)) of the variables,and causing different neural networks to learn patterns for steady stateoperations and patterns for non-steady state operations, respectively.

A description will hereinafter be made of certain basic calculationmethods at the neural network. Firstly, an operation in which signalvalues Y₁ -Y_(n) are multiplied by the corresponding weight factorsW_(ji) and the resulting products are then summed (multiplication andsumming operation) is expressed by the following formula: ##EQU1## whereY_(i) (1): a value of X_(i) of the input layer (first layer), W_(ji)(2←1): a weight factor for the route from the i^(th) variable in theinput layer (first layer) to the j^(th) neuron element model in thehidden layer (second layer), and Z_(j)(2) : the sum of inputs to thej^(th) neuron element model in the hidden layer (second Layer).

At the neuron element model 701, an output value therefrom is calculateddepending on the magnitude of Z_(j)(2), namely, in accordance with thefollowing formula:

    Y.sub.j(2) =1/(1-e.sup.-Z.sbsp.j(2))                       <2>

Details of the calculation by the formula 2 have such a relationship asshown in FIG. 5. Namely, as illustrated by the graph of the samedrawing, a value ranging from "0" to "1" is obtained as Y_(j)(2)corresponding to the value of Z_(j)(2). The calculated value Y_(j)(2) isdelivered further to the output layer, where a similar calculations areexecuted. The outline of the manner of calculation in the neural networkwill now be described. The above-described value Y_(i)(1) of eachvariable X_(i) is inputted to the input layer shown in FIG. 3. Thissignal (value) is outputted to the neuron element models of the hiddenlayer. At the neuron element models of the hidden layer, the sumZ_(j)(2) of products of these output values Y_(i)(1) and weight factorWij(2←1) is calculated in accordance with the formula 1. Based on themagnitude of the calculation result, the output value Y_(j)(2) to theoutput layer is determined in accordance with the formula 2. Similarly,the sum Z_(j)(3) of products of the output values Y_(j)(2) from thehidden layer and weight factors Wij (3←2) between the hidden layer(second layer) and the output layer (third layer) is calculated inaccordance with the following formula: ##EQU2## where Y_(i)(2) : a valueof the hidden layer (second layer), Wji(3←2): a weight factor for theroute from the i^(th) variable in the hidden layer (second layer) to thej^(th) neuron element model in the output layer (third layer), andZ_(j)(3) : the sum of inputs to the j^(th) neuron element model in theoutput layer (third layer).

Further, depending on the magnitude of Z_(j)(3), the output valueY_(j)(3) to the output layer 730 is calculated in accordance with thefollowing formula:

    Y.sub.j(3) =1/(1-e.sup.-Z.sbsp.j(3))                       <4>

In the manner described above, the value Y_(j)(3) calculated by theoutput layer is obtained.

To perform learning by the neural network, a comparison layer 740 andthe teacher signal layer 750 are also provided after the output layer730 as shown in FIG. 3, whereby the signals 730S from the output layer730 and teacher signals 750S from the teacher signal layer 750 areinputted to the comparison layer 740 and are compared to each otherthere. The magnitudes of the weight factors Wji(3←2) and the weightfactors Wji(2←1) are adjusted to reduce the errors. When thecalculations by the formula <1>-<4> and the comparison with the teachersignals are conducted again by using the weight factors thus adjusted,errors are obtained similarly. The magnitudes of the weight factorsWji(3←2) and Wji(2←1) are adjusted again to reduce the errors. Theweight factors Wji are repeatedly adjusted as described above until theerrors are reduced sufficiently. The errors are obviously large at thebeginning because the weight factors are set at random (i.e., occur asrandom numbers). The values of the output signals gradually approach thevalues of the corresponding teacher signals. Accordingly, thedistribution of the weight factors Wji reflects how the variables X_(j)of the output layer 730 have been determined from the variables X_(i) ofthe input layer 710.

The correction method of errors in the manner as described above iscalled the "reverse error propagation method". The known techniquedevised by Rumelhart et al. is employed. For details, reference may behad to the publication, "Rumelhart: Parallel Distributed Processing,Vol. 1, MIT Press (1986)"

Such learning per se is known. However, the particular feature of thepresent invention resides in that a neural network is caused torepeatedly learn plural patterns of a group of variables at differenttime points to have the neuron circuit model equipped with abilitycomparable with the past experiences of an operator. The plural patternsare patterns P_(i) (t_(i)) which were judged important by the operatorin the past. In this manner, pattern recognition ability comparable withthe past experiences of the operator is reflected to the distribution ofthe weight factors W_(ji) of the neural network, accumulated and stored.

In the embodiment described above, the calculations were performed byway of example at the neural processor 70. They can also be executed atthe system processor 42. In either case, the system must have thefunction of the neural processor 70 described above because the presentinvention applies a neural network for supporting an operation.

A description will next be made of the supporting step 72 shown in FIG.2. In this step, the operation is supported based on the results of thelearning in the learning step 71. As has been described above, the pastexperiences are reflected to the distribution of the values of theweight factors W_(ij) in the neural network. By inputting to the inputlayer a pattern P_(i) (t_(i)) consisting of values Y_(i) (t_(i)) of thevariables X_(i) at the present time point t, values of the controlvariables X_(j) set as outputs are therefore calculated as a result.

This calculation is performed by using the neural network composed ofthe input layer 710, the hidden layer 720 and the output layer 730 asshown in FIG. 6 which is a portion of FIG. 3.

According to this calculation, the formulae <1>-<4> are calculated usingthe pattern P_(i) (t_(i)) at the present time point. This calculationwill be called "perception".

The results of the perception are shown on the display 46 to support theoperation. Namely, the operator can operate with reference to theresults thus displayed. To perform the support automatically, therespective control signals 56BS are set to give a target pattern C(P_(i)(t)) which corresponds to the results of perception at the time of thepattern P_(i) (t). Since values to be calculated by perception aredependent on patterns learned, patterns to be learned must be typicalpatterns at different time points or patterns at abnormal time points tobe noted, as described above.

The knowledge extraction step 73 will now be described. In thisknowledge extraction step 73, knowledge and candidate knowledge areextracted from the results of the learning. The extraction method isperformed in three stages. An extraction method attracted to the weightfactors will be described firstly. In the relationships between thevariables X_(i) assigned to the input layer 710 and the variables Y_(j)assigned to the output layer 730, the variables X_(i) can be consideredas causes while the variables Y_(j) can be regarded as results. Thenumber of variables X_(i) affecting the variables X_(j) is thereforelimited in the calculation of the perception for deriving the variablesX_(j) from the variables X_(i). Because the degree of influence isgoverned by the weight factors W_(ij), the greater the absolute valuesof the weight factors W_(ij), the greater the degree of the influence.However, the perception of the variables X_(j) from the variables X_(i)requires the execution of the calculation formulae <1>-<4 > by theneural network constructed of plural layers while using Wji(←1) andWji(3←2). It is therefore difficult to ascertain which weight factor orfactors are predominant. Therefore, as an index systematicallyevaluating the relationships between the variables X_(i) (causes) andthe variables Y_(j) (results), an "causality index" I_(ji) defined bythe following formula is employed. ##EQU3##

where m is the number of neuron element models in the hidden layer.Since the above formula calculates the sum of products of all weightfactors through every route from X_(i) to X_(j), it indicates the degreeof influence from the variable X_(i) to the variable X_(j). In theknowledge extraction step 73, the calculation by the formula <5> isconducted first of all. If the causality index I_(ji) takes a largevalue, there is a high chance of existence of a causal relationshipbetween the variable X_(i) and the variable X_(j). Thus, thisrelationship is registered as knowledge or candidate knowledge in theknowledge base 60A or candidate knowledge base 60B in the knowledgediagnosis step 74 to be described subsequently. Representing a referencevalue for judgment by I* and the sign of Wjk(3←2)• KWki(2←1) by Q, it isconsidered, as will be shown by the following equation, that a positivecausal relationship exists when Q is positive but a negative causalrelationship exists when Q is negative on the contrary. For example,

     Knowledge 1! When I.sub.ji >I* and Q>0, the variable X.sub.j increases with the variable X.sub.i                                 <6>

     Knowledge 2! When I.sub.ji >I* and Q<0, the variable X.sub.j decreases with the variable X.sub.i                                 <7>

Incidentally, when I_(ji) ≦I*, it is deemed that no causal relationshipexists between the variable X_(i) and the variable X_(j). Further,although a detailed description is omitted, a more careful and precisecounter-measure is feasible if the magnitude of the value of I_(ji) isused as a membership function for the fuzzy inference instead of makingthe judgment only by the comparison with the judgment reference valueI*. In other words, the causality index can be used in a method forautomatically obtaining a membership function.

Details of the diagnosis (enquiry) will herein˜after be describedspecifically.

(a) Enquiry about causality index:

(a1) If I_(ji) >I*,

Make an enquiry to the operator about the relationship between X_(i) andX_(j) (for example, the relationship between <6> or <7>). If a causalrelationship is found to exist, register it in the knowledge base 60A.If no causal relationship is found to exist, register it in thecandidate knowledge base 60B.

(a2) If I_(ji) <I*,

Register the relationship between X_(i) and X_(j) in the candidateknowledge base 60B without enquiry to the operator, or in someinstances, abandon it.

When I_(ji) <I*, elimination of the line 702 connecting the variablesX_(i) and X_(j) to each other can simplify the network structure. Thisbrings about the advantageous effect that the calculation speed isimproved. It may also be contemplated of changing the number of hiddenlayers itself. Such a modification of the construction may preferably beconducted after learning a limited number of times.

It is also possible to automatically conduct these operations withoutmaking enquiries to the operator on the basis of the respectivejudgments. In this case, the operations can be performed in thefollowing manner.

(a) Enquiry about causality index:

(a1) If I_(ji) >I*,

Register the relationship between X_(i) and X_(j) in the knowledge base60A.

(a2) If I_(ji) <I*,

Register the relationship between X_(i) and X_(j) in the candidateknowledge base 60B.

Knowledge and candidate knowledge are classified as described above.There is however the possibility that even when classified as candidateknowledge, the candidate knowledge may actually be knowledge which theoperator has not notice. If a phenomenon associated with particularcandidate knowledge registered in the candidate knowledge base 60Boccurs plural times, make an enquiry again to the operator to diagnosewhether the particular candidate is reasonable as knowledge or not.

In the operation supporting step 75, the operation is supported by usinga group of knowledge inputted beforehand in the knowledge base 60A.Namely, knowledge already ascertained (empirical, scientific knowledgeor the like) is stored in advance in the knowledge base 60A. A widevariety of knowledge is therefore progressively stored in the knowledgebase 60A. The inference system 61 is driven based on the knowledge toderive a conclusion, which is then shown on the display 46. The data ofthe memory 54 are also used as needed. As an inference method, aconventional technique such as forward inference or backward inferencecan be applied. Using the result, outputted are the signals 56B whichdetermine the target pattern C(P_(i) (t_(i)).

In the manner described above, the present invention causes a neuralnetwork to learn the history of operations at different time points inthe past as needed, so that operations similar to those conducted in thepast can be supported. Further, the acquisition of knowledge can beeffected either through interaction with an operator or automatically.This method makes it possible not only to perform a similar operation toan operation in the past but also to increase the knowledge with time.Similarly to the operator, the neural network can also learn actualresults and acquire experiences, whereby the neural network has thefunction in which it can become wiser.

Another embodiment will next be described with reference to FIG. 7. Thisembodiment has a similar construction to the above-described embodimentof FIG. 2 except for the following three features (a), (b) and (c):

(a) Patterns P₁ (t₁) and P₂ (t₂) at different time points are used atthe same time as the pattern to be inputted to the input layer.

(b) The difference between the patterns, P₁,2 (t₁,2)=P₁(t₁)-P2(t2), isemployed.

(c) Plural neural networks are provided independently for respectivefunctions to cause them to learn different patterns (for example, apattern in a steady state and another pattern in a non-steady state).

A single new pattern composed of P₁(t₁), P₂(t₂) and P₁,2(t₁,2) is storedin the pattern file 71S as illustrated in FIG. 7. This new pattern islearned in the learning step 71. Here, a different value is set as thetime interval τ (=t₁ -t₂) between time t₁ and time t₂ depending on thetime-dependent variation characteristics and control characteristics ofa process whose operation is to be supported. The manner of learning isas described above with reference to FIG. 2.

In the supporting step 72, the operation is supported based on theresults of the learning. In this step, a perceptive calculation isexecuted upon receipt of a pattern P_(i) (t_(i)) at a present time,another pattern P_(i-1) (t_(i-1)) at a time point time τ before thepresent time, and the difference P_(i-1),i (t_(i-1),i) between both thepatterns. The calculation method is as described above with reference toFIG. 2.

Operations in the knowledge extraction step 73, knowledge diagnosis step74 and operation supporting step 75 are as described above withreference to FIG. 2.

The embodiment of FIGS. 1 and 2 has the advantage that a history over along period of time is learned. The embodiment described with referenceto FIG. 7 however has the two advantages that (a) learning of aphenomenon which varies in a short period of time (on the order of timeτ) can be performed effectively and (b) the intention of an operator canbe effectively reflected by using neural networks which have learnedrespective patterns classified in accordance with their functions.Namely, the advantage (a) is particularly effective when the controlvariable X_(j) is controlled in different manners depending upon whetherthe value Y_(i) (t) of the variable X_(i) is increasing or decreasing.For example, the advantage (a) is effective in performing control at thetime of a start-up or stop of a plant or in supporting control at anon-normal time or an abnormal time. On the other hand, the advantage(b) is effective especially when the manner of operation of a plantdiffers depending on the situation (the pattern of the variable X_(i)).Usually an operation at a normal time and an operation at a non-normaltime are conducted in accordance with different guidelines and manners.In such a case, it is effective to independently provide a neuralnetwork for a normal time and another neural network for a non-normaltime and to use them selectively depending on the situation.

As has been described above, the present embodiment learns the historyof past operations which are different from one another in situation,thereby making it possible to support operations similar to such pastoperations. Further, upon acquisition of knowledge, it is also possibleto acquire one or more causal relationships which vary in a short timeperiod. This makes it possible to provide a support such that anoperator can perform an operation of a plant more precisely.

The present invention has heretofore been described in a general term,taking its application to a water purifying plant by way of example.Details of specific support and control will be described by thefollowing specific examples.

A support and control system for an operation of a flocculation andsettling process can be obtained provided that, as input items, themeasurement items of the instrument 5A, i.e., water temperature,turbidity, alkalinity, pH, electrical conductivity, residual chlorineconcentration, chlorine demand, water quantity and water level, themeasurement items of the image pickup means 5B and image processor 40,i.e., characteristic quantities of flocs and the turbidity measured bythe turbidimeter 5C are inputted to the input layer, and also providedthat the flocculant feed pump control signal 12AS, alkali agent feedpump 12BS, stirrer control signals 13S, and stirring paddle controlsingals 17AS, 17BS, 17CS are set as teacher signals. This system canperform of support/control for a flocculant feeding operation,support/control for an alkali agent feeding operation, support/controlfor a high-speed stirring operation, support/control for a low-speedstirring operation, and so on. Further, the image pick-up means 5B canbe provided in the high-speed mixing basin 10, settling basin 16 orfiltering basin 17 to use the processing results of an image therefrom.

Here, a description is made of a specific example, in which the presentinvention is applied to the feeding of a flocculant to a water purifyingplant, and its effects.

The construction of a neural circuit model in this example is shown inFIG. 8 which corresponds to FIG. 3. The input layer 710 in this neuralcircuit model contains five neuron element models 701, to which thetemperature, turbidity, alkalinity, pH and flow rate of raw water attypical times on representative days are inputted. Further, the hiddenlayer 720 is constructed of three neuron element models while the outputlayer 730 and teacher layer 730 are each constructed of one neuronelement model which is assigned to a flocculant feeding rate. In alearning step, the qualities of raw water on pre-selected days (the timeshould be set at a particular time beforehand) are inputted to cause theneural circuit model to learn it. This learning step corresponds to thelearning step 71 described with reference to FIG. 2. In learning, daysto be referred to for the purpose of supporting the operation areselected at first. It is important to cause the neural circuit model tolearn days on which the treatment was successfuly, because leaning of aday on which the treatment failed results in wrong learning. Next,selection of a day on which learning should be effected is alsoimportant. In particular, variations in water quality and variations inseason give significant influence to the ability of treatment at a waterpurifying plant. Such typical days (treatment conditions) asrepresenting year-around variations thoroughly are therefore chosen. Forexample, one day is chosen per month as a pattern to be learned. Astandard for this selection is as follows:

(1) Where good treatment results are obtained, for example, in theexample of the water purifying plant, choose at least one day on whichthe turbidity of the setting basin is 1.0 mg/M or less.

(2) Choose at least one typical day where the water quality variesthroughout the year, namely, choose at least one typical day in each ofspring, summer, autumn and winter in the example of the water purifyingplant.

In the present example, as days on which the treatment was successful(i.e., the turbidity of the setting basin was below the prescribedvalue), the water qualities on ten days (one condition per day) areinputted out of the operation history within 1 year (365 days) whilehand, the neural circuit model is caused to learn the correspondingflocculant feeding rates as teacher signals. Five to thirty days aresuitable as the number of days to be learned.

After the learning was completed, unlearned conditions (on the 355 days)were inputted to the learned neural circuit model of FIG. 8 to cause theneural circuit model to perceive the corresponding flocculant feedingrates. Perception was conducted by a construction similar to that ofFIG. 8 except for the elimination of the comparison layer 740 and theteacher signal layer 750, in other words, by a construction equivalentto that of FIG. 6. As a result, the perception error (which is definedby an average of the differences between perceived values and thecorresponding values actually measured) was about 20%. Although thisperception error is equivalent to about 3 mg/M when converted into aflocculant feeding rate, this system is practically usable forsupporting the operation of the water purifying plant. According to thisspecific example, the learning of only the small number of days out ofthe days in the whole year permitted perception of flocculant feedingrates for days of various water qualities over the long time period.Reference is next had to FIG. 9, in which illustrated is an exemplaryneural circuit model construction for the case where not only thetypical water quality at a certain time point in a day but also thewater quality or qualities at one or more other time points aresimultaneously used as inputs to the neural network. This constructionis equivalent to the construction of FIG. 7 except that the differencepattern is not used among the input patterns. Although the "other timepoints" may be one time point or plural time points, this example willbe described taking a case in which one other time point will beconsidered. Namely, to the input layer 710, the values of the watertemperature, turbidity, alkalinity, pH and flow rate of the raw water ata present time and those in the past (at a time point a predeterminedtime interval before the present time) are inputted. The input layer 710therefore requires 10 neuron element models. Five neuron element modelsare used for the hidden layer 720. Further, one neuron element model isemployed for each of the output layer 730 and the teacher signal layer750 to which a flocculant feeding rate is assigned in a similar mannerto FIG. 8. The present water quality to be inputted to the input layercorresponds to P1(t1), while the past water quality also to be inputtedto the input layer corresponds to P2(t2.). As these past values, theinformation on the water quality five hours before is used in thisexample. Accordingly, in this example, in view of both the water qualityat the time point of each learning and the water quality five hoursbefore, it is learned how the flocculant feeding rate should be set forthe former time point. The time point in the past is not necessarilylimited to five hours before, and the time point one hour before ortwelve hours before may be employed. The neural network was caused tolearn the water qualities on ten days and the corresponding flocculantfeeding rates. As a result, the perceptive error was 17%. The error wasimproved over the example of FIG. 8 because time-dependent variations ofthe water quality were taken into consideration in the input layer.

The neural network of FIG. 10 shows further use of information ontime-dependent differences as input information. This neural networkcorresponds to that described with reference to FIG. 7. In this example,time-dependent variations of the water quality are explicitly inputtedas differences. Namely, inputted simultaneously to the input layer 710are (1) present values of the water temperature, turbidity, alkalinity,pH and flow rate of the raw water , (2) their past values, and (3) thedifferences between the present values and the corresponding pastvalues. Correlating these inputs to the inputs in FIG. 7, P1(t1)corresponds to the present water quality, P2(t2) to the past waterquality, and P1,2(t1,2)=P1(t1)-P2(t2.) to the differences therebetween.In this example, fifteen neuron element models are used in the inputlayer 710, and seven neuron element models in the hidden layer 720.Using one neuron element model in each of the output layer 730 and theteacher signal layer 750 as in the aforementioned example, theflocculant feeding rate is assigned thereto. As a result of learning ina similar manner as in the preceding example, the perceptive error inone year was improved up to 12%. Namely, as a result of explicit use oftime-dependent variations of the water quality in the form of values ofdifferences as input information in this example, it has become possibleto more accurately predict the injection rate when the water qualityvaries significantly, for example, at the beginning or end of rain fall.

The accuracy can be improved further provided that floccurant feedingrates in the past are added as past values.

Although not illustrated in the drawing, two neural networks of the sametype as the neural networks of FIG. 10 were provided independently, onefor fine weather as a normal time (raw water turbidity: 10 mg/l orlower) and the other for rainy weather as a non-normal time (raw waterturbidity: 10 mg/l or higher). They were caused to learn independently,followed by perception in a similar manner to the examples describedabove. As a result, the perceptive error was improved up to 7%. Thismeans that the manner of operation by a well-experienced operator wasreproduced more faithfully owing to the consideration of both rainy timeand fine time or both the beginning and ending of rain fall. As isenvisaged from the foregoing, the additional consideration oftime-dependent difference information and the independent use of pluralneural networks for different functions can bring about the advantageouseffect that the perceptive error (i.e., the guidance error in supportingan operation) can be reduced further.

Next, the previously-described conventional method, in which therelationship between measured information (the quality of raw water) andoutput information (the feeding rate of a flocculant) are converted intoa model, will be compared specifically in effects with the presentexample. As a conventional method, a general multiple regressionanalysis was employed. According to this method, the year-around data onthe quality of raw water and flocculant feeding rate are all used torepresent the relationship between the quality of raw water and thecorresponding feeding rate of flocculant by means of a formula. Theerror of the flocculant feeding rate over one year was calculated bythis method. The calculation gave an error of about 14%. In the presentembodiment of the invention, although the data of only 20 days in totalwere employed in this example, according to the specific examples ofFIG. 10 et seq, there examples have been found to be at least aseffective as the conventional method which uses the data of one year.

Although not described in this embodiment, input of informationavailable from monitoring of images such as the state of floc formationcan bring about one or more advantages additionally. Further, although adetailed description is omitted, the combination of input patters is notlimited to those described above and may include the combination ofpresent values and differences or the combination of past values anddifferences.

As other application examples of the present invention in the form ofthe system of FIG. 1, a support and control system for a feedingoperation of chlorine can be obtained provided that, as input items, themeasurement items of the instrument 5A, i.e., water temperature,turbidity, alkalinity, pH, electrical conductivity, residual chlorineconcentration, chlorine demand, water quantity and water level, themeasurement items of the image pickup means 5B and image processor 40,i.e., characteristic quantities of flocs, the turbidity measured by theturbidimeter 5C, the measurement items of the instrument 5D, i.e., headloss, water level, turbidity, residual chlorine concentration, pH andflow rate, and the measurement items of the instruments 5E and 5F, i.e.,water levels, turbidities, residual chlorine concentrations, pH's, flowrates, water pressures and water temperatures are inputted to the inputlayer, and also provided that the chlorine feeder control signal 26S isset as a teacher signal. On the other hand, setting of the pump controlsignal 19S as a teacher signal results in a support and control systemfor a filtration process.

Further, a support and control system for the control of the waterquality and quantity in a distributing pipe network can be obtainedprovided that the measurement items of the instruments 5E, 5F, i.e.,water levels, turbidities, residual chlorine concentrations, pH's,turbidities, flow rates, water pressures and water temperatures and themeasurement items of the instrument 5G, i.e., turbidity, residualchlorine concentration, pH, flow rate, water pressure and watertemperature are inputted to the input layer, and also provided that thepump control signal 22S and the valve control signals 23AS, 23BS, 23CSare set as teacher signals. This system can be employed to support andcontrol the operation in connection with the distribution flow rate andwater pressure.

In each of the foregoing examples, the operation is conducted inaccordance with the past history and/or operation results.Advantageously, they all have the functions that an operation can beperformed in the light of the actual results and the precedence,knowledge can be acquired automatically and the quality of support andcontrol is progressively improved, although such functions have beenhardly realized by conventional operation supporting systems in whichconventional automatic control engineering and/or knowledge engineeringis applied.

In addition, although a detailed description is omitted, the presentinvention can of course carry out various controls relating to themaintenance and management of a water purifying plant, such as waterlevel control.

A further embodiment of the present invention will next be described.Similarly to the preceding embodiments, the present invention is appliedto support an operation of a water purifying plant.

In this embodiment, the learning of a neural network is performed withplural past patterns of (1) weather, (2) quantities of solar radiationat different time points in the respective seasons, (3) the quantity,quality and temperature of water measured, and (4) the feeding rate orconcentration of chlorine. The above information (1) to (3) on a givenday are inputted to the neural network so learned (i.e., the neuralnetwork for prediction), whereby the neural network is allowed topredict the unknown value (4) to control the feeding rate orconcentration of pre-chlorine.

The execution process comprises the following steps: (1) learning by thelearning neural circuit model (learning neural network), (2) predictionof the feeding rate or concentration of chlorine through association bythe learned neural network (predicting neural network), and (3) controlof the feeding rate or concentration of chlorine on the basis of theresult of the prediction.

The construction and operation are now described based on the embodimentof FIG. 11. First of all, the flow of the water purifying plant isdescribed. In the drawing, raw water guided from a river, lake or pond(not shown) is introduced into the receiving basin 9. The raw water isthen guided from the receiving basin 9 to the high-speed mixing basin10, where a flocculant is injected from the flocculant tank 11A,followed by stirring by the stirrer 13 to induce flocculation of fineparticles diffused in the raw water. Although omitted in the drawing,alkali agent may be injected as needed. In the floc-forming basin 15(only one basin is shown in the drawing for the sake of simplicityalthough there are plural floc-forming basins), flocs (flocculatedmasses) are allowed to grow under stirring by the stirring paddle 17.The flocs thus grown are allowed to settle in the settling basin 16, andthe supernatant is filtered in the filtering basin 17. The water thusfiltered is temporarily stored in the purified water basin 20 and thewater distribution basin 21, and is then delivered by the pump 22 toindividual customers by way of the distributing pipe network 24. Forsterilization, chlorine is injected by chlorine feeders 26A, 26B atsuitable rates into the receiving basin 9 and the purified water basin20 from the chlorine tank 25 (in which liquid chlorine or sodiumhypochlorite is stored). The chlorine injected into the water receivingbasin 9 is called "pre-chlorine", while that injected into the purifiedwater basin 20 is called "post-chlorine".

The water receiving basin 9, the floc-forming basin 15, the settlingbasin 16, an outlet of the filtering basin 17, the purified water basin20 and the water distribution basin 21 are provided with instruments 9M,15M, 16M, 17M, 20M and 21M, respectively. Items to be measured includewater temperature, turbidity, alkalinity, pH, electrical conductivity,residual chlorine concentration, chlorine demand, organic substances,iron, manganese, ammoniacal nitrogen, organic nitrogen, water quantity,water level, head loss, and the like. Also provided is an actinometer 8Mwhich measures the quantity of solar radiation (the intensity of light,the intensity of ultraviolet rays, etc.).

Next, the construction of a prediction/operation control unit 80 will bedescribed. The prediction/ operation control unit 80 is a computersystem. To facilitate the description of the present invention, a flowdiagram of processing is depicted in FIG. 11. Incidentally, the portionsindicated as "network" in the drawing include a number of wirings (orcommunication routes for information) corresponding to the solid lines702 in FIG. 4.

Firstly, a past pattern data file 71F in the prediction/operationcontrol unit 80 stores time-series data of (1) the weather which anoperator 101 inputs via a communication means 46, (2) season, time, andthe quantity of solar radiation measured by the actinometer 8M, (3) thequantity, quality and temperature of water measured by the instruments9M, 15M, 16M, 17M, 20M, 21M, and (4) the chlorine feeding rate at thechlorine feed pump 26A or the chlorine concentration (the averagechlorine concentration after injection). From the past pattern data file71F, (1) a selected data stream T of weather, (2) a selected data streamN of season, time and solar radiation quantity, (3) a selected datastream C of water quantity, quality and temperature and (4) a selecteddata stream Q of chlorine feeding rate or concentration are alloutputted to the learning neural network 71 either automatically orresponsive to an instruction 71S from the communication means 46.Learning is then performed using these data T, N, C, Q, and the results71S1 and 71S2 of the learning are outputted to the predicting neuralnetwork 72. At the predicting neural network 72, the data T, N, C, Qwhich are required for a prediction are selected from the content of thepast pattern data file 71F and are used for the prediction. The resultsof the prediction are displayed as prediction signals 72S at thecommunication means 46 and are also outputted to the operation controlstep 75. Upon receipt of the prediction signals 72S, the operationcontrol step 75 outputs a target value signal 75S for the chlorinefeeding rate to the chlorine feed pump 26A to control the feeding rate.At the same time, the target value signal 75S of the feeding rate isdisplayed at the communication means 46, and the actual feeding rate maythen be corrected as needed, namely, if so chosen by the operator. Thecorrected value is again outputted as the target value signal 75S forfeeding rate to the chlorine feed pump 26A.

A description will next be made of the operation of the embodiment ofFIG. 11. In the following description, control of the chlorine feedingrate will be described by way of example. It is however to be noted thatcontrol of the average chlorine concentration after the injection ofchlorine can be effected similarly.

First of all, a description will be made of a method for storing data inthe past pattern data file 71F. Past pattern data at a time point t=0comprise (1) weather T(0,1), (2) season, time and quantity of solarradiation N(0,1),N(0,2),N(0,3), (3) water quantity, quality andtemperature C(0,1),C(0,2),C(0,3), and (4) chlorine feeding rate Q(0,1).The water quality C(0,2) is represented by the above-described pluralmeasurement data at the time point t=0. Since the accuracy is obviouslyimproved when these measurement data are all employed, a specificdescription of a case where plural measurement data are used is omittedherein. In this embodiment, the water quantity will be collectivelyrepresented by the symbol C(0,2). Data of these T, N, C and Q are storedin the past pattern data file 71F. Repeating this, data at t=0, -1, -2,. . . are successively stored. Although the present embodiment will bedescribed by setting this time interval at one hour, no particularlimitation is imposed on the practice of the present invention no matterwhether the time interval is shorter or longer than one hour.

The operation of the learning neural network 71 in the learning stepwill next be described. At the learning neural network 71, learning isperformed upon receipt of selected data from the content of the pastpattern data file 71F. The manner of selection and learning of thesedata will hereinafter be described with reference to FIGS. 11 and 12.

As is depicted in FIG. 12, in regard of (1) the weather T(0,1), (2) theseason, time and quantity of solar radiation N(0,1),N(0,2),N(0,3), (3)the water quantity, quality and temperature C(0,1),C(0,2),C(0,3) and (4)the chlorine feeding rate Q(0,1), the operation history is gone backfrom a given time point t=t1 as a basis, and their corresponding data att1-1, t1-2, . . . are learned firstly. Similarly, using t=t2(t2≠t1) as abasis, the pattern data at t2-1, t2-2, . . . are also learned, whereby qpieces of pattern data are learned in total. The learning is conductedby dividing this group of data into input data and teacher data. As isshown in FIG. 1, the input layer 710 is inputted successively with1!T(t,1), N(t,1), N(t,2), N(t,3), C(t,1), C(t,2) and C(t,3) at a giventime point (t=t1, t1, . . . ) and with T(t-1,1), N(t-1,1), N(t-1,2),N(t-1,3), C(t-1,1), C(t-1,2), C(t-1,3) and Q(t-1,1) at a time point goneback from the given time point to the past. Here, it is to be noted thatQ(t-1,1) has been added. On the other hand, the teacher layer 750 isinputted with Q(t,1). Learning is performed at the learning neuralnetwork 71, which is constructed of the input layer 710, the hiddenlayer 720, the output layer 730, the comparison layer 740 and theteacher layer 750 and corresponds to the neural circuit model of FIG. 3.

A prediction step by the predicting neural network 72 will next bedescribed. The construction of the predicting neural network 72 isequivalent to that of the neural circuit model of FIG. 6. As is depictedin FIG. 11, the chlorine feeding rate is predicted based on the resultsof the learning at the learning neural network 71, namely, thedistribution of weight factors Wji at the predicting neural network 72.Values of the weight factors Wji (3←2) and Wji (2←1) which have beenobtained as a result of the learning, are outputted as signals 71S1 and71S2 to the predicting neural network 72. Using these signals, aprediction is conducted. Incidentally, the learning neural network 71and the predicting neural network 72 are independently illustrated forthe sake of convenience in FIG. 11. Needless to say, a single neuralnetwork can be used commonly.

The operation of the predicting neural network 72 will next be describedwith reference to FIGS. 6 and 13. As is illustrated in FIG. 6, thepredicting neural network 72 has a similar construction to the learningneural network 71 except for the omission of the comparison layer 740and the teacher layer 750. What is performed at the predicting neuralnetwork 72 is to predict (4) an unknown chlorine feeding rate Q on thebasis of (1) the weather T, (2) the season, time and quantity of solarradiation N and (3) the water quantity, quality and temperature C, whichcan be predicted or are known at the present time. For this purpose,variable values Y_(i) set taking t=0 as a basis are inputted as an inputpattern to the input layer 710. The variable values Yi are, as shown inFIG. 11 or FIG. 13, 1! the weather T(0,1), (2) the season, time andquantity of solar radiation N(0,1),N(0,2), N(0,3), (3) water quantity,quality and temperature C(0,1),C(0,2),C(0,3), all of which are known atthe present time, as well as 2! the data of the variables (1)-(3) and(4) the chlorine feeding rate Q(-1,1) in the past (t=-1). The values T,N, C an Q at t=-2 may be inputted. They are all either data of actualvalues or already-known data. Based on these values, it is predicted howmuch chlorine should be injected at the present time, in other words, anunknown target chlorine feeding rate Q(0,1)* is predicted. The resultsare outputted to the output layer 730. As the calculation procedureuntil the output, the same calculation as that described above, whichmakes use of the formulae <1>through <4>, are executed. The targetchlorine feeding rate Q(0,i)* is a signal 72S.

The operation control step 75 will hereinafter be described. In theoperation control step 75, upon receipt of the signal 72S indicative ofthe target chlorine feeding rate Q(0,1)*, a signal 75S indicative of thetarget value of the chlorine feeding rate is outputted to the chlorinefeed pump 26A to control the flow rate of chlorine. The frequency ofcalculation of the signal Q(0,i) is generally every first hour, but thistime interval can be set as desired. Of course, a shorter time intervalleads to a more improved prediction accuracy. If the chlorine feedingrate for a short time period (e.g., 1 minute) cannot be predictedbecause of the setting of an unduly long time interval (1 hour in thisembodiment), it should be predicted by mathematical interpolation. Atthe same time, the signal 75S indicative of the target value of thefeeding rate (the results of the prediction) is displayed at thecommunication means 46, whereby the actual flow rate may be corrected atthe choice of the operator 101 as needed. For example, a correction suchas multiplication of the predicted flow rate by a proportion factor K1and addition of a constant K2 is effected. The value so corrected isoutputted again as the target injection rate value signal 75S to thechlorine feed pump 26A and the chlorine feed pump 26A injects chlorineat the corrected target rate into the receiving basin 9.

The present embodiment has been described taking as a target value thechlorine feeding rate which is actually controlled. Since the objectiveis to control the chlorine concentration in the water of the waterpurifying plant, a method in which the post-injection chlorineconcentration is used as a target is practical. In this case, thepost-injection average chlorine concentration which can be calculated bythe following formula <5> is used instead of the chlorine feeding rateQ(t,1)1. ##EQU4## where QD(0) is the flow rate of water under treatment.Described specifically, the control is performed as described aboveexcept for the use of Qc(0) in lieu of Q(t,1).

A further embodiment of this invention will next be described.

In this embodiment, a neural network is caused to learn a plurality ofpast patterns of disturbance factors and a plurality of past patterns ofactual demands, and disturbance factors on a given day are inputted tothe thus-learned neural network to cause the neural network to predict ademand pattern for the given day. The execution process comprises thefollowing steps: (1) learning by a learning neural circuit model(learning neural network), (2) prediction of a demand pattern throughassociation by the learned neural network (predicting neural network),and (3) operational control of the feeding rate on the basis of theresults of the prediction.

The present embodiment can be applied for the prediction of a waterdemand pattern at a water purifying plant, the prediction of a flow ratepattern of sewage flowing into a sewage treatment plant, the predictionof an electric power demand at a power generation plant or a centrallocal-dispatching office, the prediction of a thermal demand pattern ata regional cooling and heating plant, the prediction of anelectrical/thermal demand pattern at a cogeneration plant, etc. Here,the construction and operation of this embodiment will be described onthe basis of the example of FIG. 14 in which water demand patterns are awater purifying plant in to be predicted.

First of all, the overall construction of the system shown in FIG. 14will be described. In the drawing, purified water which has beenproduced at the water purifying plant and contains chlorine injectedtherein is stored in the water distribution basin 21. The purified waterin the water distribution basin 21 is delivered to the pump 22 through aline 21P1 and then to customers via a piping 21P2. Although not shown inthe drawing, the piping 21P2 is provided with a valve for the control ofboth pressure and flow rate. Time-series data of the flow rate, whichare operational data of the pump 22, are successively stored in the pastpattern data file 71F in the prediction operation control unit 80. Thepredicting operation control unit 80 is a computer system, and a flowdiagram of its processing is shown in FIG. 14 to facilitate thedescription of the present invention. Further, the portions indicated as"network" in the drawing correspond to the solid lines 702 in FIG. 4.

As the time series data of the flow rate signal 22Q, hourly cumulativedata are employed as 22Q and values Q(t,i) (t: day, i: time) are stored.On the other hand, the operator 101 inputs weather, a day of the week,an average temperature, the highest temperature, the temperature at arepresentative time, the quantity of solar radiation, the existence ornon-existence of an vent and its scale, and the like as disturbancesignals 46G in the past pattern data file 71F of theprediction/operation control unit 80 via the communication means 46which comprises a display, a keyboard or voice input unit, etc. As analternative, data signals of the above-mentioned weather, etc. can alsobe inputted externally to the communication means 46 through acommunication line 461. Needless to say, the disturbance signals 46G areinputted automatically when they are measured by automatic measurementinstruments. In this manner, the flow rate signals 22Q and disturbancesignals 46G are successively stored in the history pattern data file71F.

From the past pattern data file 71F, a selected data stream Q of theflow rate signals 22Q and a selected data stream G of the disturbancesignals 46G are both outputted to the learning neural network 71 eitherautomatically or in accordance with an instruction 71S from thecommunication means 46. Learning is performed using these data Q and G,and the results 71S1, 71S2 of the learning are outputted to thepredicting neural network 72. For a prediction, the predicting neuralnetwork 72 uses the learning results 71S1, 71S2 so received and a datastream of the flow rate signals 22Q and a data stream of the disturbancesignals 46G, said data streams having been selected from the pastpattern data file 71F. The results of the prediction are displayed as apredicted signal 72S at the communication means 46 and also outputted tothe operation control step 75. In the operation control step 75, uponreceipt of the predicted signal 72S, a target value signal 75S for theflow rate is outputted to the pump 22 to control the flow rate. At thesame time, the target value signal 75S for the flow rate is displayed atthe communication means 46 and the actual flow rate may be corrected atthe choice of the operator 101 as needed. The corrected value is againoutputted as the flow rate signal 75S to the pump 22.

The operation of the present embodiment will next be described withreference to FIG. 14.

A description will first be made of the method for storing the data ofpast patterns in the past pattern data file 71F. The past pattern dataare composed of flow rate signal 22Q and disturbance signal 46G.Expressing the flow rate signal 22Q of the pump 22 by Q(t,i) (t: day, i:time), the data from 1:00 to 24:00 of a given day (t=0) are expressed asQ(0,1), Q(0,2), Q(0,3), . . . Q(0,24). These data are outputted to thepast pattern data file 71F. When this is repeated every day, the data att=0 , -1, -2, . . . are successively stored. On the other hand,expressing by G(t,j) (t: day, j: disturbance number) the disturbancesignals 49G to be inputted through the communication means 46, theweather=G(0,1), the day of the week=G(0,2), the average temperature=20G(0,3), the highest temperature=G(0,4), the temperature at therepresentative time=G(0,5), the quantity of solar radiation=G(0,6), theexistence or non-existence of an event=G(0,7), the scale of theevent=G(0,8) and the like on the given day (t=0) are converted intotheir corresponding numbers. These data are outputted to the pastpattern data file 71F.

Next, the operation of the learning neural network 71 in the learningstep will be described. The learning neural network 71 receives selecteddata from the past pattern data file 71F. This data selection methodwill hereinafter be described. Regarding the disturbance signals G(t,j),in view of the block of the learning neural network 71 of FIG. 14 andselected times (days) t =t1, t.2., t3, . . . , the disturbance signalsof the desired number of days which go back to the past from the day tof selection are used. On the day t (t=t1, t2, t3.), G(t,1), . . . ,G(t,8) are inputted to the input layer 710 and, as to the preceding day,G(t-1,1), . . . , G(t-1,8) are inputted. However, the weather G(0,1),the day of the week G(0,2), the existence or non-existence of an eventG(0,7) and the scale of the event G(0,8) are inputted after convertingthem into symbols or numbers. As to the flow rate Q(t,i) on the otherhand, the flow rates Q(t,1), . . . , Q(t,24) over 24 hours are inputtedto the teacher signal layer 750. To the input layer 710, data Q(t-1,1),. . . , Q(t-1,24) of the preceding day (t=-1) and data of a desirednumber (p) of days going back the preceding days to the past areinputted. Although the accuracy of a prediction is improved if thisnumber of days (p) is large, the data of the preceding day (t=-1) andtwo days before (t=-2) or so are sufficient from the practicalviewpoint. Further, expressing plural past time points (q time points)by t=t1, t2, t.3., . . . , tq, the patterns at the time pointst.MDSD/i.MDNM/(i=1, 2, . . . , tq) are inputted as patterns to belearned.

The predicting step by the predicting neural network 72 will next bedescribed. The construction of the predicting neural network 72 issimilar to the neural circuit model shown in FIG. 6. As is shown in FIG.14, a demand pattern is predicted at the predicting neural network 72 onthe basis of the results of the learning at the learning neural network71, namely, the distribution of the weight factors Wji. Values of theweight factors Wji(3←2) and Wji(2←1) which have been obtained as aresult of the learning, are outputted to the predicting neural network72. Using these signals, a prediction is conducted. At first, as inputpatterns, values of variables Yi set by taking the given day (t=0) as abasis--predicted values of G for the given day and values of G and Q onthe preceding day and two days preceding the given day) are inputted tothe input layer 710. A demand pattern is next outputted as an outputpattern to the output layer 730 in accordance with calculations whichuse the formulae <1>-<4>. The demand pattern is Q(0,i)* (i=1 to 24)inside the output layer 730 in FIG. 14. This calculation is called"prediction" in the present embodiment. Since a value calculated byprediction is dependent on patterns learned, patterns to be learnedmust, as described above, be typical patterns at different time pointsor those at such abnormal times that require attention.

This prediction is carried out before starting the operation on thegiven day (usually, one day before). Namely, the patterns of Q and G forthe two days up to two days preceding the given day and predicted valuesof the disturbance factors on the given day are inputted. Then, demandpatterns are predicted for the given day by the neural network.

In the operation control step 75, upon receipt of the signal 72Sindicative of each demand pattern Q(0,i)* (i=1 to 24), a flow ratetarget value signal 75S is outputted to the pump 22 to control the flowrate. The demand patterns Q(0,i)* are flow rates at the intervals of 1hour in this embodiment. These time interval can however be set asdesired. Of course, a shorter time interval leads to a more improvedaccuracy of prediction. If the flow rate for a short time period (e.g.,1 minute) cannot be predicted because of the settling of an unduly longtime interval (1 hour in this embodiment), it should be predicted bymathematical interpolation. At the same time, the signal 75S indicativeof the target value (prediction results) of the flow rate is displayedat the communication means 46, whereby the actual flow rate may becorrected at the choice of the operator 101 as needed. For example, acorrection such as multiplication of the predicted flow rate by aproportion factor K1 and addition of a constant K2 is effected. Thevalue so corrected is outputted again as the flow rate signal 75S to thepump 22 and the pump 22 feed water only at the flow rate.

A still further embodiment will now be described with reference to FIG.15. This embodiment also relates to the treatment of sewage.

According to the present embodiment, a neural network is caused to learnthe history of (1) physical, chemical and biological characteristics ofwater flowing into a sewage treatment plant, (2) the quantity of stateof the plant, (3) season and time, (4) the quantity of evaluation ofplant state and (5) the quantity of operation of the plant, whereby theneural network is allowed to automatically acquire knowledge on thevariables (1), (2), (3) and (4) and also knowledge on the variables (1),(2), (3) and (5). At the same time, in accordance with the associationfunction of the neural network so learned, a guidance is provided forthe operation (including control) of the plant.

An execution process comprises the following steps: (1) learning by alearning neural circuit model (learning neural network), (2) acquisitionof knowledge, (3) diagnosis of knowledge 74, (4) inference, (5)association and prediction by the so-learned neural network (predictingneural network), and (6) operation and control of the plant.

The construction and operation of the present embodiment will bedescribed on the basis of the example shown in FIG. 15. Firstly, theflow of the sewage treatment process will hereinafter be described. Atthe settling basin 9, substances floating in the sewage flowed thereintoare partly removed by gravity sedimentation at first. Sewage overflowedfrom the settling basin 9 and return sludge from a return sludge line16P1 flow into an aeration tank 15, first of all. Air is fed to theaeration tank 15 from a blower 17Z via control valves 17X, 17Y, so thatthe sewage and the return sludge are mixed and agitated. The returnsludge (activated sludge) absorbs oxygen from the air, and organicsubstances in the sewage are therefore decomposed. The thus-treatedsewage is then guided to a final settling basin 16. At the finalsettling basin 16, the activated sludge is allowed to settle by gravitysedimentation, and the supernatant is discharged. The activated sludgesettled in the final settling basin 16 is drawn out and a portion of theactivated sludge thus drawn out is discharged as a surplus sludge by apump 16C2 through a surplus sludge line 16P2. The remaining majorportion of the activated sludge, said major portion having not beendischarged, is returned as return sludge from a return sludge pump 16C1to the aeration tank 15 via the return sludge line 16P1.

A description will next be made of measuring instruments. The initialsettling basin 9, aeration tank 15 and final settling basin 16 areprovided with measuring instruments 9M, 15M and 16M, respectively. Itemsto be measured include the quantity of sewage flowed in, theconcentration of floating substances, chemical oxygen demand, pH,nitrogen concentration, ammonia concentration, nitrate nitrogenconcentration, nitrite nitrogen concentration, phosphorus concentration,dissolved oxygen concentration, sludge volume index (SVI), etc. as wellas image information on bacteria and floating substances.

The construction of the prediction/operation control unit 80 will bedescribed next. The prediction/ operation control unit 80 is a computersystem, and a flow diagram of its processing is shown in FIG. 15 tofacilitate the description of the present invention. Further, theportions indicated as "network" in the drawing include a number ofwirings (or communication routes for information) corresponding to thesolid lines 702 in FIG. 4.

Stored in the past pattern data file 71F are time-series data of (1)characteristics of flowed-in water measured by the measuring instrument9M, (2) the quantities of state of the plant measured by the measuringinstruments 15M, 16M, (3) season and time, (4) the quantities ofevaluation of the plant state, i.e., some of quantities measured by themeasuring instruments 15M, 16M, (5) plant operating quantities of thepump 16C2, the return sludge pump 16C1, the blower 17Z, the controlvalves 17X, 17Y, and the like.

To the learning neural network 71, (1) a selected data row D1 of thecharacteristics of the flowed-in water, (2) a selected data row D2 ofthe quantities of state of the plant, (3) a selected data row D3 of theseason and time, (4) a selected data row D4 of the quantities ofevaluation of the plant state and (5) a selected data row D5 of theplant operating quantities are all outputted from the past pattern datafile 71F either automatically or in accordance with the instruction 71Sform the communication means 46. Although each data stream actuallycontains plural items, it will be expressed by the representativesymbols D1-D5 to facilitate the description in the present embodiment.At the learning neural network 71, learning is performed using the dataD1, D2, D3, D4, D5. The results 71S1, 71S2 of the learning are bothoutputted to each of the knowledge acquiring step 73 and the predictingneural network 72.

In the knowledge acquiring step 73, knowledge on the variables (1), (2),(3) and (4) and knowledge on the variables (1), (2), (3) and (5) areconverted into their corresponding symbols or words on the basis of thesignals 71S1, 71S2. In the knowledge diagnosing step 74, the knowledgeobtained in the knowledge acquiring step 73 is stored in the candidateknowledge base 60B or knowledge base 60A responsive to an instruction(not shown) from the communication means 46. The inference system 61performs inference upon receipt of knowledge from the knowledge base 60Aand pre-inputted knowledge from a knowledge base 60C, and outputs asignal 61S to the operation control step 75.

On the other hand, the predicting neural network 72 chooses data, whichare required for a prediction, from the past pattern data file 71F anduses them for the prediction. As the predicted signal 72S, the resultsof the prediction is displayed at the communication means 46 and is alsooutputted to the operation control step 75.

In the operation control step 75, signals 75S are outputted responsiveto the predicted signal 72S and the signal 61S, whereby (5) the plantoperating quantities of the pump 16C2, the return sludge pump 16C1, theblower 17Z, the control valves 17X, 17Y, and the like are controlled. Atthe same time, the control target value signal 75S is displayed at thecommunication means 46, and the quantity of actual control is correctedat the choice of the operator 101 as needed. The value thus corrected itagain outputted.

Next, the operation of the present invention will be described withreference to FIG. 15.

First of all, a description will be made of the method for storing datain the past pattern data file 71F. Past pattern data D1(0)-D5(0) at thetime t=0 are stored in the past pattern data file 71F. This is repeatedto successively store data at t=0, -1, -2, . . . . This embodiment willbe described assuming, by way of example, that the time interval be 1hour. This setting of the time interval does not impose any particularlimitation on the practice of the present invention.

The operation of the learning neural network 71 in the learning stepwill be described hereinafter. At the learning neural network 71,learning is carried out upon receipt of selected data from the pastpattern data file 71F. The manner of selection and learning of thesedata will be described below.

With respect to D1-D5, the time is gone back to the past from a giventime point t =ti as a basis and the pattern data at t1-1, t2-2, . . .are learned at first. Similarly, going back from t=t2 (t2≠t1) as abasis, the pattern data at t1-1, t1-2, . . . are learned. Accordingly, qpieces of pattern data are learned in total. Upon selection of these qpieces of patterns, it is desirable to choose them from typical pastpatterns. The learning is effected by dividing this group of data intoinput data and teacher data. As is shown in FIG. 15, the input layer 710is inputted successively with D1(t)-D3(t) at the given time point t(t=t1, t1, . . . ) and D1(t-1)-D5(t-1) at a time point gone back fromthe time point t to the past. It is to be noted here that D4(t-1),D5(t-1) are added. Then, a similar operation is performed with respectto the pattern data at t=t-2, t-3, . . . . On the other hand, theteacher layer 750 is inputted with D4(t) and D5(t). The learning iscarried out at the learning neural network 71 which is constructed ofthe input layer 710, the hidden layer 720, the output layer 730, thecomparison layer 740 and the teacher layer 750.

At the learning neural network 72, unknown values of the variables (4)and (5) are predicted corresponding to the variables (1), (2) and (3).For this purpose, variable values Y_(i) (D1(i)-D3(0)) set for t=0 as abasis and variable values Yi(D1(i)-D5(i), i=-1, -2, . . . ) set fort=-1, -2, . . . as basis are inputted at input patterns to the inputlayer 710. These variable values Yi consists, as shown in FIG. 1, of 1!the known values of the variables (1), (2) and (3) at the present timeand 2! the values of the variables (1)-(5) at the past time points(t=-1, -2, . . . ). It is to be noted that these values are all eitheractual values or known data. Calculations of the above formulae <1>-<4>are performed based on these values, and (4) unknown plant stateevaluation quantities (the quality of treated water) D4*(0) and (5)unknown plant operation quantities (return/surplus sludge quantity,aerating air quantity) D5*(0) are outputted to the output layer 730.Regarding the quantities (4), their signals are displayed as a guidanceat the communication means 46. As to the quantities (5), a controlsignal 72S is outputted to the operation control step 75.

The operation control step 75 will herein-after be described. In theoperation control step 75, the signal 72S corresponding to D5*(0) andthe result signal 61S from the inference system 61 are received. Theirconformability are checked. If the signal 72S is not contradictory withthe signal 61S, the signal 75S is outputted as a target value of thequantity of an operation of the plant to the pump 16C2, the returnsludge pump 16C1, the blower 17Z, the control valves 17X, 17Y, etc. Ifthere is a contradiction on the contrary, the contradiction is reportedto the operator through the communication means 46 so that a correctionis effected. Although the frequency of the control is every hour in thisembodiment, this time interval can be set as desired. Of course, asmaller time interval leads to a more improved accuracy of prediction.If the chlorine feeding rate for a short time period (e.g., 1 minute)cannot be predicted because of the setting of an unduly long timeinterval (1 hour in this embodiment), it should be predicted bymathematical interpolation. At the same time, the signal 75S indicativeof the target value of the feeding rate (the results of the prediction)is displayed at the communication means 46, whereby the actual operationmay be corrected at the choice of the operator 101 as needed.

A still further embodiment is illustrated in FIG. 16.

This embodiment relates to a method for outputting a target value ofcontrol by a transcendental model on the basis of observed quantities ofa process. The term "transcendental model" in this embodiment means aprocess model or control model already known with respect to a targetprocess. The present embodiment includes a method for automaticallyproducing an "error prediction model" to predict an error to beoutputted from the transcendental model and a method for automaticallyproducing a "parameter adjustment model" to optimize parameters of thetranscendental model. Since the models are both corrected by theoperator, each of the models is called the "operator model" in thepresent embodiment. After first describing one embodiment of the formerwith reference to FIG. 16, the latter will be described.

FIG. 16 shows one embodiment as applied to the control of injection of aflocculant in a water purifying process. The flow of the water purifyingplant will be described at first. In the drawing, raw water isintroduced into the receiving basin 9 from a river, lake or pond (notshown). The high-speed mixing basin 10 receives water from the receivingbasin 9, to which the flocculant is injected by the flocculant feed pump12 from the flocculant tank 11. The stirring blade 14 is driven by thestirrer 13. In some instances, an alkali agent may be injected topromote formation of flocs. This is however omitted. The floc-formingbasin 15 receives water from the high-speed mixing basin 10 and allowsflocs (aggregates of fine particles) to grow. The floc-forming basin 15(there are usually plural floc-forming basins but the rest of thefloc-forming basins are omitted in this embodiment) is provided with thestirring paddle 17A which rotates slowly. The flocs are allowed tosettle in the settling basin 16, and the supernatant is filtered in thefiltering basin 17. For sterilization, chlorine is injected at suitablerates into the receiving basin 9 and the purified water basin (notshown) from the chlorine tank 25 by the chlorine feeder 26.

A description will next be made of measuring instruments. To measure thequality of the raw water, the water receiving basin 9 is provided withthe measuring instrument 9M. Items to be measured include watertemperature, turbidity, alkalinity, pH, electrical conductivity,residual chlorine concentration, chlorine demand, water temperature(sic.), water level, etc. The floc-forming basin 15 is provided with themeasuring instrument 15M. The measuring instrument 15M includes meansfor measuring the items to be measured by the above measuring instrument9M and, in addition, underwater image pick-up means such as a marinecamera and image processing means. The settling basin 16 is providedwith the measuring instrument 16M. If necessary, the high-speed mixingbasin 10 is provided with a measuring instrument 10M similar to themeasuring instrument 15M. The filtering basin 17 is provided with themeasuring instrument 17M. Items to be measured by these measuringinstruments are the same as those to be measured by the above measuringinstruments 9M, 15M. The above measurement items, operation factors (theflocculant feed pump 12, stirrer 13, stirring paddle 17A) and quantitiesmaking up the operator model (including a prediction error to bedescribed subsequently and the parameters of the transcendental model)will be called "observed quantities" in the present embodiment.

Next, the outline of the construction and operation of theprediction/operation control unit 80 will be described. Theprediction/operation control unit 80 is a computer system. To facilitatethe description of the present invention, a flow diagram of processingis depicted in FIG. 16. A transcendental model calculation step 65receives data of measured quantities (parameters Pi of the measuringinstruments 9M, 15M, 16M, flocculant feed pump 12, stirrer 13, stirringpaddle 17A, prediction error En(t), and transcendental model) andoutputs signals Sc to a control quantity calculation step 67. On theother hand, the past pattern data file 71F successively stores the dataof the measured quantities. At an operator model 73, the learning neuralnetwork 71 receives a selected data row from the past pattern data file71F to perform learning. The predicting neural network 72 receives thesignals 71S1, 71S2 from the learning neural network 71 and outputssignals En*(t). In the control quantity calculation step 67, uponreceipt of the signals En*(t) or a correction value En(t) by theoperator 101 and the signals Sc, signals St are outputted. In theoperation control step 75, the signals 75S are outputted upon receipt ofthe signals St to control the flocculant feed pump 12, stirrer 13 andstirring paddle 17A. The communication means 46 communicates via theoperator 101 with the past pattern data file 71F, transcendental modelcalculation step 65, learning neural network 71, predicting neuralnetwork 72 and operation control step 75. Incidentally, theconstructions of the learning neural network 71 and predicting neuralnet work 72 are as shown in FIG. 3 and FIG. 6, respectively.

Next, the operation of the operation control unit 80 will be describedin detail.

The transcendental model calculation step 65, upon receipt of data ofobserved quantities, outputs signals to a feed forward control model(hereinafter called "FF model") 65F and a feedback control model(hereinafter called "FB model") 65B, respectively. Here, the FF model65F and FB model 65B are merely exemplary transcendental models.Therefore, transcendental models other than feedback/feed forward modelscan obviously be employed. Each of the FF model 65F and FB model 65B,upon receipt of the observed quantities Xi(t), outputs a signal which isadapted to control the injection of the flocculant. The general formulaof the FF model 65F is represented by the following formula:

    Sf=Ff(Xi(t),Pi)                                            (i)

where Sf: output signal from the FF model 65F, X.i(t): observedquantities at the time t, and Pi: parameters of the FF model.

An illustrative specific formula model of the formula (i) is representedby the following formula:

    Sf=P1•(X1(t)).sup.P2 +P3                             (ii)

where X1(t): the turbidity of raw water, P1, P2, P3: parameters.

The general formula of the FB model 65B is represented by the followingformula:

    Sb=Fb(Xi(t),Pi)                                            (iii)

where Sb: output signal from the FB model 65B, Xi(t): observedquantities at the time t, and Pi: parameters of the FB model.

An illustrative specific formula model of the formula (ii) isrepresented by the following formula:

    Sb=P1•(X2(t)-X2(t-τ))                            (iv)

where X2(t): the turbidity at the output of the settling basin at thetime t, X2(t-2): the tabidity at the output of the settling basin at thetme t-τ, P1: parameter.

Although only X1(t) and X2(t) are contained by way of example on theright-hand sides of the formulae (ii) and (iv), other observedquantities can also be used obviously.

A model output value calculation step 65A, upon receipt of the signalsSf and Sb, outputs a signal Sc to the control quantity calculation step67. Its general formula is represented by the following formula:

    Sc=Fc(Sf, Sb)                                              (v)

An exemplary specific formula model of the formula (v) can be expressedby the following formula:

    Sc=Sf+Sb                                                   (vi)

Therefore, the transcendental model 65 outputs the signal Sc, whichpredicts a quantity of the flocculant to be injected, on the basis ofthe observed quantities Xi(t) (i=1-n). Since a difference arises betweenthe predicted value Sc and the actual value in the past (it is to benoted that the latter value is a value added with the correction valueEn(t) inputted by the operator and is more correct than the predictionby the transcendental model), this "prediction error" (operator'scorrection value) is outputted from the neural network instead of theoperator.

A description will next be made of a method for storing observedquantities in the past pattern data file 71F. The observed quantitiesXi(0) (i=1 to n) at the time t=0 are stored in the past pattern datafile 71F. This is repeated to successively store quantities observed att=0, -1, -2, . . . . The time interval is for example 1 hour. It ishowever to be noted that no limitations whatsoever will be imposed onthe practice of the present invention by the setting of this interval.

The operator model 73 will next be described. First of all, theoperation of the learning neural network 71 in the learning step will bedescribed. The manner of selection of data and learning at the learningneural network 71 will now be described. With respect to Xi(t) (i=1 ton), going back to the past from a given time point t=t1 as a basis, thedata observed at t1-1, t1-2, . . . are learned at first. The value k oft1-k can be chosen as desired. Similarly, using t=t2 (t2≠t1) as a basis,the pattern data at t2-1, t2-2, . . . are learned, whereby q pieces ofpattern data are learned in total. These q pieces of patterns maydesirably be selected from typical patterns in the past. Since the timeti is a given time, continuous learning can bring about the effect thatvariations in state can be coped with more easily.

The learning is effected by dividing this group of data into input dataand teacher data. The term "input data" means factors out of theobserved quantities, said factors affecting the prediction errorpertaining to the quantity of the flocculant to be injected. The term"teacher data" indicates the prediction error En(t). Further, the inputdata is a factor based on which the operator makes a judgment. On theother hand, the teacher data is a factor to be controlled by theoperator (in this embodiment, the prediction error En(t) between a valueof the quantity of the flocculant to be injected as predicted by thetranscendental model and a value actually measured). For the same ofconvenience, assume that values of the input data are Xi(t) (i=1 to n-1)and the teacher data consists of Xn(t), namely, prediction errors En(t)alone). It is to be noted that En(t) are correction values added by theoperator to the values predicted by the transcendental model.Incidentally, the combination of these input and output data can bechosen as desired in accordance with the objective.

A prediction error En(t1) at the time t=t1 is inputted to the teacherlayer 750. Inputted to the input layer 710 are X1(t1) (i=1 to n-1) andXi (t1) at t=t1-1, t1-2, . . . . As will be described subsequently, thelearning is executed at the learning neural network 71 which isconstructed of the input layer 710, the hidden layer 720, the outputlayer 730, the comparison layer 740 and the teacher layer 750. Thissimulates that the prediction error at the time t=t1 is affected by thequantities observed at that time point and in the past and theprediction errors in the past. Learning is also performed with respectto given time points t (t=t1, t2, . . . ) in a similar manner.

The operation of the predicting neural network 72 will next bedescribed. At the predicting neural network 72, variable values Yi (i=1to p) set using the present time point (t=0) as a basis are firstlyinputted as input patterns to the input layer 710. It is to be notedthat these values are all either actual values or known data. Based onthese values, the calculations of the above formula <1>-<4> areperformed, and a prediction error En*(0) at t=0 is outputted from theoutput layer 730. Namely, En*(0) is outputted in place of an error(correction value En(0)) predicted by the operator.

In the control quantity calculation step 67, the signal En*(0) and thesignal Sc are received, and St is calculated in accordance with theformula (vii), followed by the output of the same.

    St=Sc+En*(0)                                               (vii)

Namely, a value obtained by adding the error En*(0), which has beenpredicted by the operator model 63 (neural network), to the value Scpredicted by the transcendental model is used. Since the predictionerror En*(0) has already learned operator's past operation patterns, theformula (vii) makes it possible to perform control comparable to thatconducted by the operator.

The operation control step 75 will hereinafter be described. In theoperation control step 75, signals 75S are outputted upon receipt of thesignal St so that the flocculant feed pump 12, stirrer 13 and stirringpaddle 17A are controlled. The frequency of control is every hour inthis embodiment. This time interval can however set as desired. Ofcourse, a shorter time interval leads to a more improved predictionaccuracy. If an error for a short time period (e.g., 1 minute) cannot bepredicted because of the setting of an unduly long time interval (1 hourin this embodiment), it should be predicted by mathematicalinterpolation.

Pursuant to instructions from the operator 101, the communication means46 perform a modification to the filing method for the past pattern datafile 71F, a modification to the model in the transcendental modelcalculation step 65, the display of the progress in and results from thelearning neural network 71 and the predicting neural network 72, andcorrections to the signals 75S from the operation control step 75.

It is advantageous effects of the example of FIG. 16 that the error ofthe transcendental model can be consistently and progressively decreasedowing to the learning of actual results by the neural network withoutthe need for intervention by an operator and the accuracy of control canbe improved over the conventional methods.

The embodiment of FIG. 16 has been described, taking by way of examplethe method in which the error prediction model for the prediction of theerror of the transcendental model (operator's correction value) isautomatically produced at the neural network. In the above description,the operator model 63 was the error prediction model.

A description will next be made of a method for automatically producing,in the operator model 63, a "parameter adjustment model" for theoptimization of parameters of the transcendental model. This example isto automatically perform the work that an operator adjusts theparameters Pi of the FF model 65F and FB model 65B in view of theoutputs Sc from the transcendental model calculation step 65. Thisexample is different from the example of FIG. 16 in that the parametersPi of the transcendental model are inputted to the teacher signals topredict optimum values of Pi. This example will be described withreference to FIG. 17.

In the example of FIG. 17, the parameters Pi of the FF model 65F and FBmodel 65B are set by the operator 101 via the communication means 46. Inconnection with the Pi values so set, the parameters Pi (signals 65S)are inputted as some of observed quantities from the transcendentalmodel calculation step 65 to the past pattern data file 71F and arestored there. As teacher signals for the learning neural network 71,actual values of Pi inputted by the operator are used and, following asimilar procedure to that of the example of FIG. 16, the actual resultsin the past are learned. Predicted values Pi* of these Pi values areoutputted from the predicting neural network 72, and are used asparameters Pi of the FF model 65F and FB model 65B without interventionby the operator. The FF model 65F and FB model 65B performs thecalculations of the formulae (i)-(iv) by using the predicted values Pi*,and output signals Sf and Sb, respectively. In the model output valuecalculation step 65A, upon receipt of the signals Sf and Sb, signals Scare outputted to the operation control step 75. In the operation controlstep 75, signals 75S are outputted responsive to the signals Sc so thatthe flocculant feed pump 12, stirrer 13 and stirring paddle 17 arecontrolled.

It is the advantageous effects of the example of FIG. 17 that theadjustable parameters of the transcendental model can always be setoptimally without the need for intervention by a man as a result oflearning of actual results by the neural network and the accuracy ofcontrol can be improved compared to the conventional methods.

The present invention has been described on the basis of the embodimentapplied to the water purifying process. Needless to say, the presentinvention can be applied to other general processes.

In a still further embodiment of FIG. 18, the values of (1) disturbance,(2) the quantity of state of the process, (3) the quantity of evaluationand (4) the quantity of operation of the process at plural time pointsin the past are firstly converted into membership values in accordancewith predetermined membership functions, respectively. These convertedvalues are then inputted to the neural network to learn therelationships among the variables (1) to (4). From the distribution ofweight factors in the neural network so learned, a fuzzy rule withcertainty factor is derived. Based on the fuzzy rule, inference isperformed to support or control the operation. This execution processcomprises the following steps:

1) converting values of the variables (1)-(4) into the correspondingmembership values in accordance with their respective membershipfunctions;

2) learning by the learning neural network;

3) obtaining a fuzzy rule with a certainty factor from the neuralnetwork so learned;

4) diagnosing the fuzzy rule;

5) performing fuzzy inference;

6) conducting a prediction by the learned neural network; and

7) controlling an operation of the process.

One example in which the present invention is applied to a sewagetreatment process will be described with reference to FIG. 18.

The construction and operation of the sewage treatment process willhereinafter be described. At the initial settling basin 9, substancesfloating in flowed-in sewage are partly removed by gravity sedimentationat first. Sewage overflowed from the settling basin 9 and return sludgefrom the return sludge line 16P1 flow into the aeration tank 15, firstof all. Air is fed to the aeration tank 15 from the blower 17Z via thecontrol valves 17X, 17Y, so that the sewage and the return sludge aremixed and agitated. The return sludge (activated sludge) absorbs oxygenfrom the air, and organic substances in the sewage are thereforedecomposed. The thus-treated sewage is then guided to the final settlingbasin 16. At the final settling basin 16, the activated sludge isallowed to settle by gravity sedimentation, and the supernatant isdischarged. The activated sludge settled in the final settling basin 16is drawn out and a large majority of the activated sludge thus drawn outis returned as return sludge from the return sludge pump 16C1 to theaeration tank 15 via the return sludge line 16P1. The balance isdischarged as surplus sludge by the surplus sludge pump 16C2 through thesurplus sludge line 16P2.

A description will next be made of measuring instruments. The initialsettling basin 9, aeration tank 15 and final settling basin 16 areprovided with measuring the measuring instruments 9M, 15M and 16M,respectively. Items to be measured include the quantity of sewage flowedin, the concentration of floating substances, chemical oxygen demand,pH, nitrogen concentration, ammonia concentration, nitrate nitrogenconcentration, nitrite nitrogen concentration, phosphorus concentration,dissolved oxygen concentration, sludge volume index (SVI), etc. as wellas image information on bacteria and floating substances.

The construction of the prediction/operation control unit 80 will bedescribed next. The prediction/operation control unit 80 is a computersystem, and a flow diagram of its processing is shown in FIG. 15 tofacilitate the description of the present invention. Stored successivelyin the past pattern data file 71F are data measured by the measuringinstruments 9M, 15M, 16M. The data stream thus stored is outputted tothe membership conversion step 69. In the membership conversion step 69,the values of the variables (1)-(4) are converted into the correspondingmembership values and the resulting signals are outputted to thelearning neural network 71. At the predicting neural network 72, signals72S are outputted upon receipt of signals 71S1 and 71S2 from thelearning neural network 71. In a fuzzy acquisition step 73 on the otherhand, the signals 71S1 and 71S2 are received from the learning neuralnetwork 71. In a fuzzy rule diagnosis step 74, signals are received fromthe communication means 46 and the fuzzy rule acquisition step 73 arestored in a candidate fuzzy rule base 60B or a fuzzy rule base 60A. Afuzzy inference system 61 receives signals from the fuzzy rule bases60A, 60C and outputs signals 61S to the operation control step 75,whereby the surplus sludge pump 16C2, return sludge pump 16C1, blower17Z, and control valves 17X, 17Y. Pursuant to instructions by theoperator, the communication means 46 performs communication with thepast pattern data file 71F, learning neural network 71, predictingneural network 72, operation control step 75 and fuzzy rule diagnosisstep 74. Incidentally, the portions indicated as "network" in thedrawing include a number of wirings (or communication routes forinformation) corresponding to the solid lines 702 in FIG.

The operation of the prediction/operation control unit 80 will next bedescribed.

The past pattern data file 71F stores time-series data of (1)disturbance characteristics measured by the measuring instrument 9M, (2)the quantities of state of the process measured by the measuringinstrument 15M, (3) the quantities of evaluation of the process measuredby the measuring instrument 16M, and (4) process operating quantities ofthe surplus sludge pump 16C2, the return sludge pump 16C1, the blower17Z, the control valves 17X, 17Y, and the like.

In the membership conversion step 69, data rows D1, D2, D3, D4 selectedfrom the values of (1) the disturbance characteristics, (2) thequantities of state of the process, (3) the quantities of evaluation ofthe process and (4) the process operating quantities are received fromthe past pattern data file 71F either automatically or in accordancewith the instruction 71S from the communication means 46, and are thenconverted into the corresponding membership values. The data rowsindividually contain plural items as a matter of fact, but these itemsare represented by their representative symbols D1 to D4 in the presentembodiment so as to facilitate the description.

The learning neural network 71 performs learning using the membershipvalues converted above, and the results 71S1 and 71S2 of the learningare outputted to the fuzzy rule acquisition step 73 and the predictingneural network 72, respectively.

In the fuzzy rule acquisition step 73, the candidate fuzzy rulesrelating to the variables (1) to (4) are converted into thecorresponding symbols or words on the basis of 71S1 and 71S2. In thefuzzy rule diagnosis step 74, the fuzzy rules obtained in the fuzzy ruleacquisition step 73 are stored in the candidate fuzzy rule base 60B orthe fuzzy rule base 60A in accordance with an instruction from thecommunication means 46. The fuzzy inference system 61 performs inferenceupon receipt of the fuzzy rule 60A and the pre-inputted fuzzy rule base60C and then outputs signals 61S to the operation control step 75.

At the predicting neural network 72 on the other hand, data required forprediction are selected from the past pattern data file 71F and are usedfor prediction. Signals 72S representing the results of the predictionare displayed at the communication means 46 and are also outputted tothe operation control step 75.

In the operation control step 75, the predicted signals 72S and signals61S are received and signals 75S are outputted, whereby processcontrolling quantities of (1) the surplus sludge pump 16C2, returnsludge pump 16C1, blower 17Z and control valves 17X, 17Y and the likeare controlled. At the same time, control target value signals 75S aredisplayed at the communication means 46, and the actual controlquantities may be corrected at the choice of the operator 101 as needed.Corrected values are outputted again.

Next, the operation of the present invention will be described in detailwith reference to FIG. 18.

First of all, a description will be made of a method for storing data inthe past pattern data file 71F. The past pattern data D1(0) to D4(0) atthe time t=0 are stored in the past pattern data file 71F. This isrepeated to successively store the past pattern data at t=0, -1, -2, . .. . The time interval may be one hour by way of example. No limitationsare imposed on the practice of the present invention by the setting ofthis time interval.

The membership conversion step 69 will be described with reference toFIG. 19, which shows illustrative conversions by preset membershipfunctions. In the drawing, a case in which the dissolved oxygenconcentration (hereinafter abbreviated as "DO") is high is illustratedas a representative of D2. Further, "high", "usual" and "lower" statesof DO are converted into converted values D2H, D2M, D2L by membershipconversion functions 692H, 692M, 692L, respectively. Incidentally, theseconverted values D2H, D2M, D2L are collectively called, and representedby a symbol DF2. The axis of abscissas of each conversion functionindicates DO as (2) the quantities of state of the process, while theaxis of ordinates shows their degrees by values ranging from 0 to 1. Asone example, a description will be made of the membership conversionfunction 691L. The membership value corresponding to DO value=0.5 is0.9. This means that, when DO value=0.5, the degree (membership value)is 0.9, in other words, this means that the "DO value is low". Bydefining a membership function for each variable in advance in thismanner, the data rows D1, D2, D3, D4 are converted into theircorresponding membership values, thereby obtaining DF2, DF3, DR4.Although DO was classified into three states "high", "usual" and "low"in this embodiment, DO can be classified into any desired number ofstates.

The operation of the learning neural network 71 in the learning stepwill hereinafter be described. The learning neural network 71 performslearning upon receipt of the membership values. The manner of these dataselection and learning will be described below. Going back to the pastfrom a given time point t=t1 with respect to DF1 (i.e., D1H, D1M, D1L)to DF4 (i.e., D4H, D4M, D4L), the pattern data at t1-1, t1-2, . . . arelearned at first. Similarly, using t=t2 (t2≠t1) as a basis, the patterndata at t2-1, t2-2, . . . are learned. Accordingly, q pieces of patterndata are learned in total. These q pieces of patterns may preferably beselected from typical patterns in the past. Since the time point ti is agiven time point, continuous learning makes it possible to automaticallyacquire new fuzzy rules which can cope with new situations.

The learning is effected by dividing this group of data into input dataand teacher data. The input layer 710 is inputted successively withDF1(t) and DF3(t) at the given time point t (t=t1, t2, . . . ) andDF1(t-1) to DF4(t-1) at a time point gone back from the time point t tothe past. It is to be noted here that DF3(t-1) and DF4(t-1) are added.Then, a similar operation is performed with respect to the pattern dataat t=t-2, t-3, . . . . On the other hand, the teacher layer 750 isinputted with D3(t) and D4(t). Although (3) DF3(t) and (4) DF4(t) wereinputted to the teacher layer in the present embodiment, theadvantageous effects of the present embodiment will not be lost whichone or more of the variables (1) to (4) are inputted to the teacherlayer. The learning is carried out at the learning neural network 71which is constructed of the input layer 710, the hidden layer 720, theoutput layer 730, the comparison layer 740 and the teacher layer 750.

In the fuzzy rule acquisition step 73, the fuzzy rules among thevariables (1), (2), (3) and (4) are converted into symbols or words onthe basis of 71S1 and 71S2. The certainty factor of a rule relating to avariable i to be set in the input layer 710 and a variable j to be setin the output layer 730 is calculated in accordance with the followingformula (viii). Incidentally, the formula (viii) has been derived by thepresent inventors by a mathematical analysis. ##EQU5## where Iji:certainty factor, and m: the number of neuron element models in thehidden layer.

The calculation of the formula (viii) is to obtain the sum of theproducts of weight factors of all routes extending from the input layerto the output layer. The rule representing the relationship between theith input and the jth output is established corresponding to thecertainty factor Iji. Iji is calculated with respect to each of thecombinations between the input layer and the output layer. The resultsare converted as a candidate fuzzy rule into the Japanese language. Forexample,

Candidate fuzzy rule!: If the ith input is large, the jth output will belarge with the certainty factor Iji.!

This conversion can be effected in such a way that the rules aresuccessively outputted with the rule having the largest certainty factorIji first and the rules are then converted into combined candidate rulesas shown below:

Candidate fuzzy rule!: If the ith input is large and the kth input isalso large, the jth output will be large with the certaintyfactor=(Iji+Ijk/2.!

In the fuzzy rule diagnosis step 74, each candidate fuzzy rule obtainedin the fuzzy rule acquisition step 73 is judged in reasonabilitypursuant to instructions from the operator and the communication means46. When judged reasonable, the candidate fuzzy rule is stored in thefuzzy rule base 60A. Otherwise, the candidate fuzzy rule is stored inthe candidate fuzzy rule base 60B. Even in the case of a candidate fuzzyrule once stored in the candidate fuzzy rule base 60B, if it occursplural times, the number of its occurrence is counted. When the numberexceeds a predetermined number (for example, twice), an enquiry is madeagain to the operator via the communication means 46 and the fuzzy rulediagnosis step 74 is repeated.

The fuzzy rule base in this embodiment contains information onrelationships such that, for (1) each disturbance and (2) each quantityof state of the process, (3) what quantities of evaluation are obtainedand (4) how the process should accordingly be operated. Depending on thecombination of these variables to be assigned to the input layer and theoutput layer, a desired fuzzy rule can be extracted based on a desiredcombination of the variables (1) to (4).

The fuzzy inference system 61 performs inference upon receipt of datafrom the fuzzy rule base 60A and fuzzy rule base 60C, and outputssignals 61S to the operation control step 75. Production rules or fuzzyrules, which have been acquired in advance by a conventional method,i.e., by an interview to the operator 101, are stored beforehand in thefuzzy rule base 60C. The fuzzy rule base 60C can be used as an auxiliarywhenever needed. The fuzzy inference system 61 performs a forward orbackward inference on the basis of a rule.

A description will next be made of the predicting step which makes useof the prediction neural network 72. The construction of the predictingneural network 72 is as shown in FIG. 6. As is illustrated in FIG. 18,the predicting neural network 72 receives the results of the learning atthe learning neural network 71, namely, the values of the weight factorsWji(3←2) and Wji(2←1) as signals 71S1 and 71S2. Although the learningneural network 71 and the predicting neural network 72 are illustratedseparately for the description of their respective processing flows inFIG. 18, a single neural network can of course be employed commonly.

The operation of the predicting neural network 72 will next bedescribed. The predicting neural network 72 has a construction similarto the learning neural network 71 except for the omission of thecomparison layer 740 and the teacher layer 750. At the predicting neuralnetwork 72, unknown values of the above variables (3) and (4) arepredicted corresponding to values of the variables (1) and (2). For thispurpose, variable values Yi(DF1(0),DF2(0)) set using t=0 as a basis andvariable values Y1(DF1(i) to DF5(i), i=-1, -2, . . . ) set using t=-1,-2, . . . as bases are inputted as input patterns to the input layer 710at first. These variable values Yi include 1! the known values of thevariables (1) and (2) at the present time and 2! the values of thevariables (1) to (4) in the past (t=-1, -2, . . . ). It is to be notedthat all of these values are either actual values or known data. Basedon these values, the calculations of the above formulae <1>-<4> areexecuted, and (4) unknown quantities of evaluation of process state (thequality of treated water) DF4(0)* and (5) unknown process operationquantities (return/surplus sludge quantities, aerating air quantity)DF5(0)* are outputted to the output layer 730. The predicted value ofthe variable (3) is displayed as a guidance at the communication means46, while the predicted value of the variable (4) is outputted ascontrol signals 72S to the operation control step 75.

The operation control step 75 will hereinafter be described. In theoperation control step 75, the signals 72S of DFS(0)* and the signals61S from the fuzzy inference system 61 are received, and theirconformability is then investigated. Unless the signals 72S contradictthe signals 61S, the signals 75S are outputted as target values of theprocess operating quantities to the surplus sludge pump 16C2, returnsludge pump 16C1, blower 17Z, control valves 17X, 17Y, etc. If theycontradict each other on the contrary, this is reported to the operator101 via the communication means 46 so as to add a correction.Incidentally, the frequency of control is every hour in this embodiment.This time interval can however be set as desired. Of course, a shortertime interval leads to a more improved prediction accuracy. If thechlorine feeding rate for a short time period (e.g., 1 minute) cannot bepredicted because of the setting of an unduly long time interval (1 hourin this embodiment), it should be predicted by mathematicalinterpolation. At the same time, the signal 75S indicative of the targetvalue of the feeding rate (the results of the prediction) is displayedat the communication means 46, whereby the actual operation may becorrected at the choice of the operator 101 as needed.

The present invention has been described taking the sewage treatmentprocess by way of example. Needless to say, the present invention canalso be applied to general processes.

According to this embodiment, the guidance and control of an operationcan be performed by automatically acquiring fuzzy rules from actual datain the past and effecting prediction by a neural network. It istherefore possible to conduct, with a smaller labor and easily from theviewpoint of engineering, "an operation which conforms with the actualresults and precedence but is equivocal" which is practiced by anoperator. Since learning can be performed at any time, it is possible tolearn and control promptly following variations in situations.

The present invention has been described on the basis of the embodimentsor examples directed to the water purifying plant. The basic concept ofthe present invention can however be applied to processes dealing withone or more phenomena which vary with time, for example, sewage or wastewater treatment processes, river information processing processes,combined co-generation systems, indoor environment control systems suchas building management systems and air conditioning, elevator managementsystems, meteorological information processing processes, thermal,nuclear and hydraulic power generation processes, transport operationmanagement systems such as train operation management systems, publicservice system such as map information systems, production processessuch as chemical processes, biological processes, semiconductorfabrication processes and food production processes, security/exchangeinformation processing processes, information systems such as bankmanagement information processes, computerized control systems, terminalmanagement systems, computer network management systems, and the like.

INDUSTRIAL APPLICABILITY

According to the present invention, a high-accuracy operation supportingsystem can be constructed rather easily by using a neural circuit modelfor the support of an operation of a process. Knowledge which iscontained, without being noticed, in information on the history ofoperations in the past can be easily extracted from a learned neuralcircuit model for use in supporting an operation.

We claim:
 1. A method for supporting an operation of a process byobtaining knowledge between state quantities from a time-dependentprocess and supporting the operation of the process using knowledge,said method comprising the steps of:providing a neural circuit model foroutputting an operation quantity of the process in accordance with adisturbance, a state quantity of the process and an evaluated quantityof the process, said neural circuit model being of a layer structurehaving an input layer, at least once hidden layer and an output layer;converting values of at least one of an operation quantity, disturbance,state quantity and evaluated quantity at plural time points in the pastin accordance with a preset membership function whereby a value of atleast a disturbance or a state quantity or of said operation quantity,disturbance, state quantity and evaluated quantity is inputted to theinput layer, and having the neuron circuit model learn by using a valueof at least one of said operation quantity, disturbance, state quantityand evaluated quantity, which value corresponds to the value inputted,as a teacher pattern for the output layer so that a fuzzy rule with acertainty factor, said fuzzy rule describing at least two relationshipsof those between said operation quantity, disturbance, state quantityand evaluated quantity, is output; and supporting or controlling theoperation of the process by using the fuzzy rule with the certaintyfactor.
 2. An apparatus for supporting an operation of a process byobtaining knowledge between state quantities from a time-dependentprocess and supporting the operation of the process using knowledge,said apparatus comprising:a storage for storing time-dependent dataconcerning a disturbance, a state quantity of the process and evaluatedquantity of the process; a converter for converting values of at leastone of an operation quantity of the process, a disturbance, a statequantity of the process and an evaluated quantity of the process atplural time points in the past in accordance with a preset membershipfunction; a neural circuit model for outputting the operation quantityof the process in accordance with the disturbance, the state quantity ofthe process and the evaluated quantity of the process, said neuralcircuit model being of a layer structure constructed of an input layer,at least one hidden layer and an output layer, a value of at least oneof the disturbance and the state quantity of the process being inputtedto the input layer, said neural circuit model performing learning byusing, as a teacher pattern for the output layer, a value of at leastone of the operation quantity of the process, the disturbance, the statequantity of the process and the evaluated quantity of the process, whichvalue corresponds to the value inputted, so that a fuzzy rule with acertainty factor is outputted, said fuzzy rule for describing at leasttwo relationships of those between the operation quantity of theprocess, the disturbance, the state quantity of the process and theevaluated quantity of the process; and one of a supporter and acontroller, said supporter supports the operation of the process byusing the fuzzy rule with the certainty factor, said controller controlsthe operation of the process by using the fuzzy rule with the certaintyfactor.